title stringlengths 9 156 | authors stringlengths 6 291 | abstract stringlengths 326 3.46k ⌀ | pdf_path stringlengths 42 154 | download_url stringlengths 88 199 | bibtex stringlengths 203 793 |
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Large-Scale Image Annotation by Efficient and Robust Kernel Metric Learning | Zheyun Feng, Rong Jin, Anil Jain | One of the key challenges in search-based image annotation models is to define an appropriate similarity measure between images. Many kernel distance metric learning (KML) algorithms have been developed in order to capture the nonlinear relationships between visual features and semantics of the images. One fundamental ... | 2013/pdf/Feng_Large-Scale_Image_Annotation_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Feng_Large-Scale_Image_Annotation_2013_ICCV_paper.pdf | @InProceedings{Feng_2013_ICCV,author = {Feng, Zheyun and Jin, Rong and Jain, Anil},title = {Large-Scale Image Annotation by Efficient and Robust Kernel Metric Learning},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Image Co-segmentation via Consistent Functional Maps | Fan Wang, Qixing Huang, Leonidas J. Guibas | Joint segmentation of image sets has great importance for object recognition, image classification, and image retrieval. In this paper, we aim to jointly segment a set of images starting from a small number of labeled images or none at all. To allow the images to share segmentation information with each other, we build... | 2013/pdf/Wang_Image_Co-segmentation_via_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Wang_Image_Co-segmentation_via_2013_ICCV_paper.pdf | @InProceedings{Wang_2013_ICCV,author = {Wang, Fan and Huang, Qixing and Guibas, Leonidas J.},title = {Image Co-segmentation via Consistent Functional Maps},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Latent Task Adaptation with Large-Scale Hierarchies | Yangqing Jia, Trevor Darrell | Recent years have witnessed the success of large-scale image classification systems that are able to identify objects among thousands of possible labels. However, it is yet unclear how general classifiers such as ones trained on ImageNet can be optimally adapted to specific tasks, each of which only covers a semantical... | 2013/pdf/Jia_Latent_Task_Adaptation_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Jia_Latent_Task_Adaptation_2013_ICCV_paper.pdf | @InProceedings{Jia_2013_ICCV,author = {Jia, Yangqing and Darrell, Trevor},title = {Latent Task Adaptation with Large-Scale Hierarchies},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Hybrid Deep Learning for Face Verification | Yi Sun, Xiaogang Wang, Xiaoou Tang | This paper proposes a hybrid convolutional network (ConvNet)-Restricted Boltzmann Machine (RBM) model for face verification in wild conditions. A key contribution of this work is to directly learn relational visual features, which indicate identity similarities, from raw pixels of face pairs with a hybrid deep network.... | 2013/pdf/Sun_Hybrid_Deep_Learning_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Sun_Hybrid_Deep_Learning_2013_ICCV_paper.pdf | @InProceedings{Sun_2013_ICCV,author = {Sun, Yi and Wang, Xiaogang and Tang, Xiaoou},title = {Hybrid Deep Learning for Face Verification},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Manipulation Pattern Discovery: A Nonparametric Bayesian Approach | Bingbing Ni, Pierre Moulin | We aim to unsupervisedly discover human's action (motion) patterns of manipulating various objects in scenarios such as assisted living. We are motivated by two key observations. First, large variation exists in motion patterns associated with various types of objects being manipulated, thus manually defining motion pr... | 2013/pdf/Ni_Manipulation_Pattern_Discovery_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Ni_Manipulation_Pattern_Discovery_2013_ICCV_paper.pdf | @InProceedings{Ni_2013_ICCV,author = {Ni, Bingbing and Moulin, Pierre},title = {Manipulation Pattern Discovery: A Nonparametric Bayesian Approach},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Real-Time Solution to the Absolute Pose Problem with Unknown Radial Distortion and Focal Length | Zuzana Kukelova, Martin Bujnak, Tomas Pajdla | The problem of determining the absolute position and orientation of a camera from a set of 2D-to-3D point correspondences is one of the most important problems in computer vision with a broad range of applications. In this paper we present a new solution to the absolute pose problem for camera with unknown radial disto... | 2013/pdf/Kukelova_Real-Time_Solution_to_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Kukelova_Real-Time_Solution_to_2013_ICCV_paper.pdf | @InProceedings{Kukelova_2013_ICCV,author = {Kukelova, Zuzana and Bujnak, Martin and Pajdla, Tomas},title = {Real-Time Solution to the Absolute Pose Problem with Unknown Radial Distortion and Focal Length},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = ... |
Recursive Estimation of the Stein Center of SPD Matrices and Its Applications | Hesamoddin Salehian, Guang Cheng, Baba C. Vemuri, Jeffrey Ho | Symmetric positive-definite (SPD) matrices are ubiquitous in Computer Vision, Machine Learning and Medical Image Analysis. Finding the center/average of a population of such matrices is a common theme in many algorithms such as clustering, segmentation, principal geodesic analysis, etc. The center of a population of su... | 2013/pdf/Salehian_Recursive_Estimation_of_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Salehian_Recursive_Estimation_of_2013_ICCV_paper.pdf | @InProceedings{Salehian_2013_ICCV,author = {Salehian, Hesamoddin and Cheng, Guang and Vemuri, Baba C. and Ho, Jeffrey},title = {Recursive Estimation of the Stein Center of SPD Matrices and Its Applications},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year ... |
Constant Time Weighted Median Filtering for Stereo Matching and Beyond | Ziyang Ma, Kaiming He, Yichen Wei, Jian Sun, Enhua Wu | Despite the continuous advances in local stereo matching for years, most efforts are on developing robust cost computation and aggregation methods. Little attention has been seriously paid to the disparity refinement. In this work, we study weighted median filtering for disparity refinement. We discover that with this ... | 2013/pdf/Ma_Constant_Time_Weighted_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Ma_Constant_Time_Weighted_2013_ICCV_paper.pdf | @InProceedings{Ma_2013_ICCV,author = {Ma, Ziyang and He, Kaiming and Wei, Yichen and Sun, Jian and Wu, Enhua},title = {Constant Time Weighted Median Filtering for Stereo Matching and Beyond},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Sieving Regression Forest Votes for Facial Feature Detection in the Wild | Heng Yang, Ioannis Patras | In this paper we propose a method for the localization of multiple facial features on challenging face images. In the regression forests (RF) framework, observations (patches) that are extracted at several image locations cast votes for the localization of several facial features. In order to filter out votes that are ... | 2013/pdf/Yang_Sieving_Regression_Forest_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Yang_Sieving_Regression_Forest_2013_ICCV_paper.pdf | @InProceedings{Yang_2013_ICCV,author = {Yang, Heng and Patras, Ioannis},title = {Sieving Regression Forest Votes for Facial Feature Detection in the Wild},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Latent Data Association: Bayesian Model Selection for Multi-target Tracking | Aleksandr V. Segal, Ian Reid | We propose a novel parametrization of the data association problem for multi-target tracking. In our formulation, the number of targets is implicitly inferred together with the data association, effectively solving data association and model selection as a single inference problem. The novel formulation allows us to in... | 2013/pdf/Segal_Latent_Data_Association_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Segal_Latent_Data_Association_2013_ICCV_paper.pdf | @InProceedings{Segal_2013_ICCV,author = {Segal, Aleksandr V. and Reid, Ian},title = {Latent Data Association: Bayesian Model Selection for Multi-target Tracking},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Feature Weighting via Optimal Thresholding for Video Analysis | Zhongwen Xu, Yi Yang, Ivor Tsang, Nicu Sebe, Alexander G. Hauptmann | Fusion of multiple features can boost the performance of large-scale visual classification and detection tasks like TRECVID Multimedia Event Detection (MED) competition [1]. In this paper, we propose a novel feature fusion approach, namely Feature Weighting via Optimal Thresholding (FWOT) to effectively fuse various fe... | 2013/pdf/Xu_Feature_Weighting_via_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Xu_Feature_Weighting_via_2013_ICCV_paper.pdf | @InProceedings{Xu_2013_ICCV,author = {Xu, Zhongwen and Yang, Yi and Tsang, Ivor and Sebe, Nicu and Hauptmann, Alexander G.},title = {Feature Weighting via Optimal Thresholding for Video Analysis},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Holistic Scene Understanding for 3D Object Detection with RGBD Cameras | Dahua Lin, Sanja Fidler, Raquel Urtasun | In this paper, we tackle the problem of indoor scene understanding using RGBD data. Towards this goal, we propose a holistic approach that exploits 2D segmentation, 3D geometry, as well as contextual relations between scenes and objects. Specifically, we extend the CPMC [3] framework to 3D in order to generate candidat... | 2013/pdf/Lin_Holistic_Scene_Understanding_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Lin_Holistic_Scene_Understanding_2013_ICCV_paper.pdf | @InProceedings{Lin_2013_ICCV,author = {Lin, Dahua and Fidler, Sanja and Urtasun, Raquel},title = {Holistic Scene Understanding for 3D Object Detection with RGBD Cameras},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Tracking via Robust Multi-task Multi-view Joint Sparse Representation | Zhibin Hong, Xue Mei, Danil Prokhorov, Dacheng Tao | Combining multiple observation views has proven beneficial for tracking. In this paper, we cast tracking as a novel multi-task multi-view sparse learning problem and exploit the cues from multiple views including various types of visual features, such as intensity, color, and edge, where each feature observation can be... | 2013/pdf/Hong_Tracking_via_Robust_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Hong_Tracking_via_Robust_2013_ICCV_paper.pdf | @InProceedings{Hong_2013_ICCV,author = {Hong, Zhibin and Mei, Xue and Prokhorov, Danil and Tao, Dacheng},title = {Tracking via Robust Multi-task Multi-view Joint Sparse Representation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
A Simple Model for Intrinsic Image Decomposition with Depth Cues | Qifeng Chen, Vladlen Koltun | We present a model for intrinsic decomposition of RGB-D images. Our approach analyzes a single RGB-D image and estimates albedo and shading fields that explain the input. To disambiguate the problem, our model estimates a number of components that jointly account for the reconstructed shading. By decomposing the shadin... | 2013/pdf/Chen_A_Simple_Model_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Chen_A_Simple_Model_2013_ICCV_paper.pdf | @InProceedings{Chen_2013_ICCV,author = {Chen, Qifeng and Koltun, Vladlen},title = {A Simple Model for Intrinsic Image Decomposition with Depth Cues},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Restoring an Image Taken through a Window Covered with Dirt or Rain | David Eigen, Dilip Krishnan, Rob Fergus | Photographs taken through a window are often compromised by dirt or rain present on the window surface. Common cases of this include pictures taken from inside a vehicle, or outdoor security cameras mounted inside a protective enclosure. At capture time, defocus can be used to remove the artifacts, but this relies on a... | 2013/pdf/Eigen_Restoring_an_Image_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Eigen_Restoring_an_Image_2013_ICCV_paper.pdf | @InProceedings{Eigen_2013_ICCV,author = {Eigen, David and Krishnan, Dilip and Fergus, Rob},title = {Restoring an Image Taken through a Window Covered with Dirt or Rain},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Online Robust Non-negative Dictionary Learning for Visual Tracking | Naiyan Wang, Jingdong Wang, Dit-Yan Yeung | This paper studies the visual tracking problem in video sequences and presents a novel robust sparse tracker under the particle filter framework. In particular, we propose an online robust non-negative dictionary learning algorithm for updating the object templates so that each learned template can capture a distinctiv... | 2013/pdf/Wang_Online_Robust_Non-negative_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Wang_Online_Robust_Non-negative_2013_ICCV_paper.pdf | @InProceedings{Wang_2013_ICCV,author = {Wang, Naiyan and Wang, Jingdong and Yeung, Dit-Yan},title = {Online Robust Non-negative Dictionary Learning for Visual Tracking},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model | Xiang Yu, Junzhou Huang, Shaoting Zhang, Wang Yan, Dimitris N. Metaxas | This paper addresses the problem of facial landmark localization and tracking from a single camera. We present a two-stage cascaded deformable shape model to effectively and efficiently localize facial landmarks with large head pose variations. For face detection, we propose a group sparse learning method to automatica... | 2013/pdf/Yu_Pose-Free_Facial_Landmark_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Yu_Pose-Free_Facial_Landmark_2013_ICCV_paper.pdf | @InProceedings{Yu_2013_ICCV,author = {Yu, Xiang and Huang, Junzhou and Zhang, Shaoting and Yan, Wang and Metaxas, Dimitris N.},title = {Pose-Free Facial Landmark Fitting via Optimized Part Mixtures and Cascaded Deformable Shape Model},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICC... |
Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes | Sukrit Shankar, Joan Lasenby, Roberto Cipolla | Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks [19, 23]. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for realworld applications. In this ... | 2013/pdf/Shankar_Semantic_Transform_Weakly_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Shankar_Semantic_Transform_Weakly_2013_ICCV_paper.pdf | @InProceedings{Shankar_2013_ICCV,author = {Shankar, Sukrit and Lasenby, Joan and Cipolla, Roberto},title = {Semantic Transform: Weakly Supervised Semantic Inference for Relating Visual Attributes},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Correlation Adaptive Subspace Segmentation by Trace Lasso | Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan | This paper studies the subspace segmentation problem. Given a set of data points drawn from a union of subspaces, the goal is to partition them into their underlying subspaces they were drawn from. The spectral clustering method is used as the framework. It requires to find an affinity matrix which is close to block di... | 2013/pdf/Lu_Correlation_Adaptive_Subspace_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Lu_Correlation_Adaptive_Subspace_2013_ICCV_paper.pdf | @InProceedings{Lu_2013_ICCV,author = {Lu, Canyi and Feng, Jiashi and Lin, Zhouchen and Yan, Shuicheng},title = {Correlation Adaptive Subspace Segmentation by Trace Lasso},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
A Max-Margin Perspective on Sparse Representation-Based Classification | Zhaowen Wang, Jianchao Yang, Nasser Nasrabadi, Thomas Huang | Sparse Representation-based Classification (SRC) is a powerful tool in distinguishing signal categories which lie on different subspaces. Despite its wide application to visual recognition tasks, current understanding of SRC is solely based on a reconstructive perspective, which neither offers any guarantee on its clas... | 2013/pdf/Wang_A_Max-Margin_Perspective_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Wang_A_Max-Margin_Perspective_2013_ICCV_paper.pdf | @InProceedings{Wang_2013_ICCV,author = {Wang, Zhaowen and Yang, Jianchao and Nasrabadi, Nasser and Huang, Thomas},title = {A Max-Margin Perspective on Sparse Representation-Based Classification},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos | Baoyuan Wu, Siwei Lyu, Bao-Gang Hu, Qiang Ji | We describe a novel method that simultaneously clusters and associates short sequences of detected faces (termed as face tracklets) in videos. The rationale of our method is that face tracklet clustering and linking are related problems that can benefit from the solutions of each other. Our method is based on a hidden ... | 2013/pdf/Wu_Simultaneous_Clustering_and_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Wu_Simultaneous_Clustering_and_2013_ICCV_paper.pdf | @InProceedings{Wu_2013_ICCV,author = {Wu, Baoyuan and Lyu, Siwei and Hu, Bao-Gang and Ji, Qiang},title = {Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Relative Attributes for Large-Scale Abandoned Object Detection | Quanfu Fan, Prasad Gabbur, Sharath Pankanti | Effective reduction of false alarms in large-scale video surveillance is rather challenging, especially for applications where abnormal events of interest rarely occur, such as abandoned object detection. We develop an approach to prioritize alerts by ranking them, and demonstrate its great effectiveness in reducing fa... | 2013/pdf/Fan_Relative_Attributes_for_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Fan_Relative_Attributes_for_2013_ICCV_paper.pdf | @InProceedings{Fan_2013_ICCV,author = {Fan, Quanfu and Gabbur, Prasad and Pankanti, Sharath},title = {Relative Attributes for Large-Scale Abandoned Object Detection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
PM-Huber: PatchMatch with Huber Regularization for Stereo Matching | Philipp Heise, Sebastian Klose, Brian Jensen, Alois Knoll | Most stereo correspondence algorithms match support windows at integer-valued disparities and assume a constant disparity value within the support window. The recently proposed PatchMatch stereo algorithm [7] overcomes this limitation of previous algorithms by directly estimating planes. This work presents a method tha... | 2013/pdf/Heise_PM-Huber_PatchMatch_with_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Heise_PM-Huber_PatchMatch_with_2013_ICCV_paper.pdf | @InProceedings{Heise_2013_ICCV,author = {Heise, Philipp and Klose, Sebastian and Jensen, Brian and Knoll, Alois},title = {PM-Huber: PatchMatch with Huber Regularization for Stereo Matching},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Subpixel Scanning Invariant to Indirect Lighting Using Quadratic Code Length | Nicolas Martin, Vincent Couture, Sebastien Roy | We present a scanning method that recovers dense subpixel camera-projector correspondence without requiring any photometric calibration nor preliminary knowledge of their relative geometry. Subpixel accuracy is achieved by considering several zero-crossings defined by the difference between pairs of unstructured patter... | 2013/pdf/Martin_Subpixel_Scanning_Invariant_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Martin_Subpixel_Scanning_Invariant_2013_ICCV_paper.pdf | @InProceedings{Martin_2013_ICCV,author = {Martin, Nicolas and Couture, Vincent and Roy, Sebastien},title = {Subpixel Scanning Invariant to Indirect Lighting Using Quadratic Code Length},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
DCSH - Matching Patches in RGBD Images | Yaron Eshet, Simon Korman, Eyal Ofek, Shai Avidan | We extend patch based methods to work on patches in 3D space. We start with Coherency Sensitive Hashing [12] (CSH), which is an algorithm for matching patches between two RGB images, and extend it to work with RGBD images. This is done by warping all 3D patches to a common virtual plane in which CSH is performed. To av... | 2013/pdf/Eshet_DCSH_-_Matching_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Eshet_DCSH_-_Matching_2013_ICCV_paper.pdf | @InProceedings{Eshet_2013_ICCV,author = {Eshet, Yaron and Korman, Simon and Ofek, Eyal and Avidan, Shai},title = {DCSH - Matching Patches in RGBD Images},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Random Grids: Fast Approximate Nearest Neighbors and Range Searching for Image Search | Dror Aiger, Efi Kokiopoulou, Ehud Rivlin | We propose two solutions for both nearest neighbors and range search problems. For the nearest neighbors problem, we propose a c-approximate solution for the restricted version of the decision problem with bounded radius which is then reduced to the nearest neighbors by a known reduction. For range searching we propose... | 2013/pdf/Aiger_Random_Grids_Fast_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Aiger_Random_Grids_Fast_2013_ICCV_paper.pdf | @InProceedings{Aiger_2013_ICCV,author = {Aiger, Dror and Kokiopoulou, Efi and Rivlin, Ehud},title = {Random Grids: Fast Approximate Nearest Neighbors and Range Searching for Image Search},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Dynamic Probabilistic Volumetric Models | Ali Osman Ulusoy, Octavian Biris, Joseph L. Mundy | This paper presents a probabilistic volumetric framework for image based modeling of general dynamic 3-d scenes. The framework is targeted towards high quality modeling of complex scenes evolving over thousands of frames. Extensive storage and computational resources are required in processing large scale space-time (4... | 2013/pdf/Ulusoy_Dynamic_Probabilistic_Volumetric_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Ulusoy_Dynamic_Probabilistic_Volumetric_2013_ICCV_paper.pdf | @InProceedings{Ulusoy_2013_ICCV,author = {Ulusoy, Ali Osman and Biris, Octavian and Mundy, Joseph L.},title = {Dynamic Probabilistic Volumetric Models},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion | Yan Yan, Elisa Ricci, Ramanathan Subramanian, Oswald Lanz, Nicu Sebe | We propose a novel Multi-Task Learning framework (FEGA-MTL) for classifying the head pose of a person who moves freely in an environment monitored by multiple, large field-of-view surveillance cameras. As the target (person) moves, distortions in facial appearance owing to camera perspective and scale severely impede p... | 2013/pdf/Yan_No_Matter_Where_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Yan_No_Matter_Where_2013_ICCV_paper.pdf | @InProceedings{Yan_2013_ICCV,author = {Yan, Yan and Ricci, Elisa and Subramanian, Ramanathan and Lanz, Oswald and Sebe, Nicu},title = {No Matter Where You Are: Flexible Graph-Guided Multi-task Learning for Multi-view Head Pose Classification under Target Motion},booktitle = {Proceedings of the IEEE International Confer... |
Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation | David Ferstl, Christian Reinbacher, Rene Ranftl, Matthias Ruether, Horst Bischof | In this work we present a novel method for the challenging problem of depth image upsampling. Modern depth cameras such as Kinect or Time of Flight cameras deliver dense, high quality depth measurements but are limited in their lateral resolution. To overcome this limitation we formulate a convex optimization problem u... | 2013/pdf/Ferstl_Image_Guided_Depth_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Ferstl_Image_Guided_Depth_2013_ICCV_paper.pdf | @InProceedings{Ferstl_2013_ICCV,author = {Ferstl, David and Reinbacher, Christian and Ranftl, Rene and Ruether, Matthias and Bischof, Horst},title = {Image Guided Depth Upsampling Using Anisotropic Total Generalized Variation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},mont... |
3D Scene Understanding by Voxel-CRF | Byung-Soo Kim, Pushmeet Kohli, Silvio Savarese | Scene understanding is an important yet very challenging problem in computer vision. In the past few years, researchers have taken advantage of the recent diffusion of depth-RGB (RGB-D) cameras to help simplify the problem of inferring scene semantics. However, while the added 3D geometry is certainly useful to segment... | 2013/pdf/Kim_3D_Scene_Understanding_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Kim_3D_Scene_Understanding_2013_ICCV_paper.pdf | @InProceedings{Kim_2013_ICCV,author = {Kim, Byung-Soo and Kohli, Pushmeet and Savarese, Silvio},title = {3D Scene Understanding by Voxel-CRF},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions | Mohamed Elhoseiny, Babak Saleh, Ahmed Elgammal | The main question we address in this paper is how to use purely textual description of categories with no training images to learn visual classifiers for these categories. We propose an approach for zero-shot learning of object categories where the description of unseen categories comes in the form of typical text such... | 2013/pdf/Elhoseiny_Write_a_Classifier_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Elhoseiny_Write_a_Classifier_2013_ICCV_paper.pdf | @InProceedings{Elhoseiny_2013_ICCV,author = {Elhoseiny, Mohamed and Saleh, Babak and Elgammal, Ahmed},title = {Write a Classifier: Zero-Shot Learning Using Purely Textual Descriptions},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Robust Object Tracking with Online Multi-lifespan Dictionary Learning | Junliang Xing, Jin Gao, Bing Li, Weiming Hu, Shuicheng Yan | Recently, sparse representation has been introduced for robust object tracking. By representing the object sparsely, i.e., using only a few templates via 1 -norm minimization, these so-called 1 -trackers exhibit promising tracking results. In this work, we address the object template building and updating problem in th... | 2013/pdf/Xing_Robust_Object_Tracking_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Xing_Robust_Object_Tracking_2013_ICCV_paper.pdf | @InProceedings{Xing_2013_ICCV,author = {Xing, Junliang and Gao, Jin and Li, Bing and Hu, Weiming and Yan, Shuicheng},title = {Robust Object Tracking with Online Multi-lifespan Dictionary Learning},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Finding the Best from the Second Bests - Inhibiting Subjective Bias in Evaluation of Visual Tracking Algorithms | Yu Pang, Haibin Ling | Evaluating visual tracking algorithms, or "trackers" for short, is of great importance in computer vision. However, it is hard to "fairly" compare trackers due to many parameters need to be tuned in the experimental configurations. On the other hand, when introducing a new tracker, a recent trend is to validate it by c... | 2013/pdf/Pang_Finding_the_Best_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Pang_Finding_the_Best_2013_ICCV_paper.pdf | @InProceedings{Pang_2013_ICCV,author = {Pang, Yu and Ling, Haibin},title = {Finding the Best from the Second Bests - Inhibiting Subjective Bias in Evaluation of Visual Tracking Algorithms},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Anchored Neighborhood Regression for Fast Example-Based Super-Resolution | Radu Timofte, Vincent De Smet, Luc Van Gool | Recently there have been significant advances in image upscaling or image super-resolution based on a dictionary of low and high resolution exemplars. The running time of the methods is often ignored despite the fact that it is a critical factor for real applications. This paper proposes fast super-resolution methods w... | 2013/pdf/Timofte_Anchored_Neighborhood_Regression_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Timofte_Anchored_Neighborhood_Regression_2013_ICCV_paper.pdf | @InProceedings{Timofte_2013_ICCV,author = {Timofte, Radu and De Smet, Vincent and Van Gool, Luc},title = {Anchored Neighborhood Regression for Fast Example-Based Super-Resolution},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Predicting an Object Location Using a Global Image Representation | Jose A. Rodriguez Serrano, Diane Larlus | We tackle the detection of prominent objects in images as a retrieval task: given a global image descriptor, we find the most similar images in an annotated dataset, and transfer the object bounding boxes. We refer to this approach as data driven detection (DDD), that is an alternative to sliding windows. Previous work... | 2013/pdf/Serrano_Predicting_an_Object_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Serrano_Predicting_an_Object_2013_ICCV_paper.pdf | @InProceedings{Serrano_2013_ICCV,author = {Serrano, Jose A. Rodriguez and Larlus, Diane},title = {Predicting an Object Location Using a Global Image Representation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach | Arash Vahdat, Kevin Cannons, Greg Mori, Sangmin Oh, Ilseo Kim | We present a compositional model for video event detection. A video is modeled using a collection of both global and segment-level features and kernel functions are employed for similarity comparisons. The locations of salient, discriminative video segments are treated as a latent variable, allowing the model to explic... | 2013/pdf/Vahdat_Compositional_Models_for_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Vahdat_Compositional_Models_for_2013_ICCV_paper.pdf | @InProceedings{Vahdat_2013_ICCV,author = {Vahdat, Arash and Cannons, Kevin and Mori, Greg and Oh, Sangmin and Kim, Ilseo},title = {Compositional Models for Video Event Detection: A Multiple Kernel Learning Latent Variable Approach},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)}... |
Nested Shape Descriptors | Jeffrey Byrne, Jianbo Shi | In this paper, we propose a new family of binary local feature descriptors called nested shape descriptors. These descriptors are constructed by pooling oriented gradients over a large geometric structure called the Hawaiian earring, which is constructed with a nested correlation structure that enables a new robust loc... | 2013/pdf/Byrne_Nested_Shape_Descriptors_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Byrne_Nested_Shape_Descriptors_2013_ICCV_paper.pdf | @InProceedings{Byrne_2013_ICCV,author = {Byrne, Jeffrey and Shi, Jianbo},title = {Nested Shape Descriptors},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Face Recognition via Archetype Hull Ranking | Yuanjun Xiong, Wei Liu, Deli Zhao, Xiaoou Tang | The archetype hull model is playing an important role in large-scale data analytics and mining, but rarely applied to vision problems. In this paper, we migrate such a geometric model to address face recognition and verification together through proposing a unified archetype hull ranking framework. Upon a scalable grap... | 2013/pdf/Xiong_Face_Recognition_via_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Xiong_Face_Recognition_via_2013_ICCV_paper.pdf | @InProceedings{Xiong_2013_ICCV,author = {Xiong, Yuanjun and Liu, Wei and Zhao, Deli and Tang, Xiaoou},title = {Face Recognition via Archetype Hull Ranking},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Detecting Dynamic Objects with Multi-view Background Subtraction | Raul Diaz, Sam Hallman, Charless C. Fowlkes | The confluence of robust algorithms for structure from motion along with high-coverage mapping and imaging of the world around us suggests that it will soon be feasible to accurately estimate camera pose for a large class photographs taken in outdoor, urban environments. In this paper, we investigate how such informati... | 2013/pdf/Diaz_Detecting_Dynamic_Objects_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Diaz_Detecting_Dynamic_Objects_2013_ICCV_paper.pdf | @InProceedings{Diaz_2013_ICCV,author = {Diaz, Raul and Hallman, Sam and Fowlkes, Charless C.},title = {Detecting Dynamic Objects with Multi-view Background Subtraction},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Coarse-to-Fine Semantic Video Segmentation Using Supervoxel Trees | Aastha Jain, Shuanak Chatterjee, Rene Vidal | We propose an exact, general and efficient coarse-to-fine energy minimization strategy for semantic video segmentation. Our strategy is based on a hierarchical abstraction of the supervoxel graph that allows us to minimize an energy defined at the finest level of the hierarchy by minimizing a series of simpler energies... | 2013/pdf/Jain_Coarse-to-Fine_Semantic_Video_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Jain_Coarse-to-Fine_Semantic_Video_2013_ICCV_paper.pdf | @InProceedings{Jain_2013_ICCV,author = {Jain, Aastha and Chatterjee, Shuanak and Vidal, Rene},title = {Coarse-to-Fine Semantic Video Segmentation Using Supervoxel Trees},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Local Signal Equalization for Correspondence Matching | Derek Bradley, Thabo Beeler | Correspondence matching is one of the most common problems in computer vision, and it is often solved using photo-consistency of local regions. These approaches typically assume that the frequency content in the local region is consistent in the image pair, such that matching is performed on similar signals. However, i... | 2013/pdf/Bradley_Local_Signal_Equalization_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Bradley_Local_Signal_Equalization_2013_ICCV_paper.pdf | @InProceedings{Bradley_2013_ICCV,author = {Bradley, Derek and Beeler, Thabo},title = {Local Signal Equalization for Correspondence Matching},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Combining the Right Features for Complex Event Recognition | Kevin Tang, Bangpeng Yao, Li Fei-Fei, Daphne Koller | In this paper, we tackle the problem of combining features extracted from video for complex event recognition. Feature combination is an especially relevant task in video data, as there are many features we can extract, ranging from image features computed from individual frames to video features that take temporal inf... | 2013/pdf/Tang_Combining_the_Right_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Tang_Combining_the_Right_2013_ICCV_paper.pdf | @InProceedings{Tang_2013_ICCV,author = {Tang, Kevin and Yao, Bangpeng and Fei-Fei, Li and Koller, Daphne},title = {Combining the Right Features for Complex Event Recognition},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Joint Subspace Stabilization for Stereoscopic Video | Feng Liu, Yuzhen Niu, Hailin Jin | Shaky stereoscopic video is not only unpleasant to watch but may also cause 3D fatigue. Stabilizing the left and right view of a stereoscopic video separately using a monocular stabilization method tends to both introduce undesirable vertical disparities and damage horizontal disparities, which may destroy the stereosc... | 2013/pdf/Liu_Joint_Subspace_Stabilization_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Liu_Joint_Subspace_Stabilization_2013_ICCV_paper.pdf | @InProceedings{Liu_2013_ICCV,author = {Liu, Feng and Niu, Yuzhen and Jin, Hailin},title = {Joint Subspace Stabilization for Stereoscopic Video},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
On One-Shot Similarity Kernels: Explicit Feature Maps and Properties | Stefanos Zafeiriou, Irene Kotsia | Kernels have been a common tool of machine learning and computer vision applications for modeling nonlinearities and/or the design of robust 1 similarity measures between objects. Arguably, the class of positive semidefinite (psd) kernels, widely known as Mercer’s Kernels,constitutes one of the most well-studied cases.... | 2013/pdf/Zafeiriou_On_One-Shot_Similarity_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zafeiriou_On_One-Shot_Similarity_2013_ICCV_paper.pdf | @InProceedings{Zafeiriou_2013_ICCV,author = {Zafeiriou, Stefanos and Kotsia, Irene},title = {On One-Shot Similarity Kernels: Explicit Feature Maps and Properties},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
NEIL: Extracting Visual Knowledge from Web Data | Xinlei Chen, Abhinav Shrivastava, Abhinav Gupta | We propose NEIL (Never Ending Image Learner), a computer program that runs 24 hours per day and 7 days per week to automatically extract visual knowledge from Internet data. NEIL uses a semi-supervised learning algorithm that jointly discovers common sense relationships (e.g., "Corolla is a kind of/looks similar to Car... | 2013/pdf/Chen_NEIL_Extracting_Visual_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Chen_NEIL_Extracting_Visual_2013_ICCV_paper.pdf | @InProceedings{Chen_2013_ICCV,author = {Chen, Xinlei and Shrivastava, Abhinav and Gupta, Abhinav},title = {NEIL: Extracting Visual Knowledge from Web Data},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Learning CRFs for Image Parsing with Adaptive Subgradient Descent | Honghui Zhang, Jingdong Wang, Ping Tan, Jinglu Wang, Long Quan | We propose an adaptive subgradient descent method to efficiently learn the parameters of CRF models for image parsing. To balance the learning efficiency and performance of the learned CRF models, the parameter learning is iteratively carried out by solving a convex optimization problem in each iteration, which integra... | 2013/pdf/Zhang_Learning_CRFs_for_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhang_Learning_CRFs_for_2013_ICCV_paper.pdf | @InProceedings{Zhang_2013_ICCV,author = {Zhang, Honghui and Wang, Jingdong and Tan, Ping and Wang, Jinglu and Quan, Long},title = {Learning CRFs for Image Parsing with Adaptive Subgradient Descent},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
A Global Linear Method for Camera Pose Registration | Nianjuan Jiang, Zhaopeng Cui, Ping Tan | We present a linear method for global camera pose registration from pairwise relative poses encoded in essential matrices. Our method minimizes an approximate geometric error to enforce the triangular relationship in camera triplets. This formulation does not suffer from the typical 'unbalanced scale' problem in linear... | 2013/pdf/Jiang_A_Global_Linear_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Jiang_A_Global_Linear_2013_ICCV_paper.pdf | @InProceedings{Jiang_2013_ICCV,author = {Jiang, Nianjuan and Cui, Zhaopeng and Tan, Ping},title = {A Global Linear Method for Camera Pose Registration},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Box in the Box: Joint 3D Layout and Object Reasoning from Single Images | Alexander G. Schwing, Sanja Fidler, Marc Pollefeys, Raquel Urtasun | In this paper we propose an approach to jointly infer the room layout as well as the objects present in the scene. Towards this goal, we propose a branch and bound algorithm which is guaranteed to retrieve the global optimum of the joint problem. The main difficulty resides in taking into account occlusion in order to ... | 2013/pdf/Schwing_Box_in_the_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Schwing_Box_in_the_2013_ICCV_paper.pdf | @InProceedings{Schwing_2013_ICCV,author = {Schwing, Alexander G. and Fidler, Sanja and Pollefeys, Marc and Urtasun, Raquel},title = {Box in the Box: Joint 3D Layout and Object Reasoning from Single Images},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year =... |
Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model | Xiao Cai, Feiping Nie, Weidong Cai, Heng Huang | Automatic image categorization has become increasingly important with the development of Internet and the growth in the size of image databases. Although the image categorization can be formulated as a typical multiclass classification problem, two major challenges have been raised by the real-world images. On one hand... | 2013/pdf/Cai_Heterogeneous_Image_Features_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Cai_Heterogeneous_Image_Features_2013_ICCV_paper.pdf | @InProceedings{Cai_2013_ICCV,author = {Cai, Xiao and Nie, Feiping and Cai, Weidong and Huang, Heng},title = {Heterogeneous Image Features Integration via Multi-modal Semi-supervised Learning Model},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
3DNN: Viewpoint Invariant 3D Geometry Matching for Scene Understanding | Scott Satkin, Martial Hebert | We present a new algorithm 3DNN (3D NearestNeighbor), which is capable of matching an image with 3D data, independently of the viewpoint from which the image was captured. By leveraging rich annotations associated with each image, our algorithm can automatically produce precise and detailed 3D models of a scene from a ... | 2013/pdf/Satkin_3DNN_Viewpoint_Invariant_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Satkin_3DNN_Viewpoint_Invariant_2013_ICCV_paper.pdf | @InProceedings{Satkin_2013_ICCV,author = {Satkin, Scott and Hebert, Martial},title = {3DNN: Viewpoint Invariant 3D Geometry Matching for Scene Understanding},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Unsupervised Domain Adaptation by Domain Invariant Projection | Mahsa Baktashmotlagh, Mehrtash T. Harandi, Brian C. Lovell, Mathieu Salzmann | Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. Th... | 2013/pdf/Baktashmotlagh_Unsupervised_Domain_Adaptation_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Baktashmotlagh_Unsupervised_Domain_Adaptation_2013_ICCV_paper.pdf | @InProceedings{Baktashmotlagh_2013_ICCV,author = {Baktashmotlagh, Mahsa and Harandi, Mehrtash T. and Lovell, Brian C. and Salzmann, Mathieu},title = {Unsupervised Domain Adaptation by Domain Invariant Projection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December}... |
Correntropy Induced L2 Graph for Robust Subspace Clustering | Canyi Lu, Jinhui Tang, Min Lin, Liang Lin, Shuicheng Yan, Zhouchen Lin | In this paper, we study the robust subspace clustering problem, which aims to cluster the given possibly noisy data points into their underlying subspaces. A large pool of previous subspace clustering methods focus on the graph construction by different regularization of the representation coefficient. We instead focus... | 2013/pdf/Lu_Correntropy_Induced_L2_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Lu_Correntropy_Induced_L2_2013_ICCV_paper.pdf | @InProceedings{Lu_2013_ICCV,author = {Lu, Canyi and Tang, Jinhui and Lin, Min and Lin, Liang and Yan, Shuicheng and Lin, Zhouchen},title = {Correntropy Induced L2 Graph for Robust Subspace Clustering},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {201... |
Detecting Curved Symmetric Parts Using a Deformable Disc Model | Tom Sie Ho Lee, Sanja Fidler, Sven Dickinson | Symmetry is a powerful shape regularity that's been exploited by perceptual grouping researchers in both human and computer vision to recover part structure from an image without a priori knowledge of scene content. Drawing on the concept of a medial axis, defined as the locus of centers of maximal inscribed discs that... | 2013/pdf/Lee_Detecting_Curved_Symmetric_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Lee_Detecting_Curved_Symmetric_2013_ICCV_paper.pdf | @InProceedings{Lee_2013_ICCV,author = {Lee, Tom Sie Ho and Fidler, Sanja and Dickinson, Sven},title = {Detecting Curved Symmetric Parts Using a Deformable Disc Model},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences | Frank Steinbrucker, Christian Kerl, Daniel Cremers | We propose a method to generate highly detailed, textured 3D models of large environments from RGB-D sequences. Our system runs in real-time on a standard desktop PC with a state-of-the-art graphics card. To reduce the memory consumption, we fuse the acquired depth maps and colors in a multi-scale octree representation... | 2013/pdf/Steinbrucker_Large-Scale_Multi-resolution_Surface_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Steinbrucker_Large-Scale_Multi-resolution_Surface_2013_ICCV_paper.pdf | @InProceedings{Steinbrucker_2013_ICCV,author = {Steinbrucker, Frank and Kerl, Christian and Cremers, Daniel},title = {Large-Scale Multi-resolution Surface Reconstruction from RGB-D Sequences},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Hierarchical Data-Driven Descent for Efficient Optimal Deformation Estimation | Yuandong Tian, Srinivasa G. Narasimhan | Real-world surfaces such as clothing, water and human body deform in complex ways. The image distortions observed are high-dimensional and non-linear, making it hard to estimate these deformations accurately. The recent datadriven descent approach [17] applies Nearest Neighbor estimators iteratively on a particular dis... | 2013/pdf/Tian_Hierarchical_Data-Driven_Descent_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Tian_Hierarchical_Data-Driven_Descent_2013_ICCV_paper.pdf | @InProceedings{Tian_2013_ICCV,author = {Tian, Yuandong and Narasimhan, Srinivasa G.},title = {Hierarchical Data-Driven Descent for Efficient Optimal Deformation Estimation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
How Do You Tell a Blackbird from a Crow? | Thomas Berg, Peter N. Belhumeur | How do you tell a blackbird from a crow? There has been great progress toward automatic methods for visual recognition, including fine-grained visual categorization in which the classes to be distinguished are very similar. In a task such as bird species recognition, automatic recognition systems can now exceed the per... | 2013/pdf/Berg_How_Do_You_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Berg_How_Do_You_2013_ICCV_paper.pdf | @InProceedings{Berg_2013_ICCV,author = {Berg, Thomas and Belhumeur, Peter N.},title = {How Do You Tell a Blackbird from a Crow?},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Recognising Human-Object Interaction via Exemplar Based Modelling | Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai, Shaogang Gong, Tao Xiang | Human action can be recognised from a single still image by modelling Human-object interaction (HOI), which infers the mutual spatial structure information between human and object as well as their appearance. Existing approaches rely heavily on accurate detection of human and object, and estimation of human pose. They... | 2013/pdf/Hu_Recognising_Human-Object_Interaction_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Hu_Recognising_Human-Object_Interaction_2013_ICCV_paper.pdf | @InProceedings{Hu_2013_ICCV,author = {Hu, Jian-Fang and Zheng, Wei-Shi and Lai, Jianhuang and Gong, Shaogang and Xiang, Tao},title = {Recognising Human-Object Interaction via Exemplar Based Modelling},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {201... |
Semantic Segmentation without Annotating Segments | Wei Xia, Csaba Domokos, Jian Dong, Loong-Fah Cheong, Shuicheng Yan | Numerous existing object segmentation frameworks commonly utilize the object bounding box as a prior. In this paper, we address semantic segmentation assuming that object bounding boxes are provided by object detectors, but no training data with annotated segments are available. Based on a set of segment hypotheses, we... | 2013/pdf/Xia_Semantic_Segmentation_without_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Xia_Semantic_Segmentation_without_2013_ICCV_paper.pdf | @InProceedings{Xia_2013_ICCV,author = {Xia, Wei and Domokos, Csaba and Dong, Jian and Cheong, Loong-Fah and Yan, Shuicheng},title = {Semantic Segmentation without Annotating Segments},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Video Synopsis by Heterogeneous Multi-source Correlation | Xiatian Zhu, Chen Change Loy, Shaogang Gong | Generating coherent synopsis for surveillance video stream remains a formidable challenge due to the ambiguity and uncertainty inherent to visual observations. In contrast to existing video synopsis approaches that rely on visual cues alone, we propose a novel multi-source synopsis framework capable of correlating visu... | 2013/pdf/Zhu_Video_Synopsis_by_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhu_Video_Synopsis_by_2013_ICCV_paper.pdf | @InProceedings{Zhu_2013_ICCV,author = {Zhu, Xiatian and Loy, Chen Change and Gong, Shaogang},title = {Video Synopsis by Heterogeneous Multi-source Correlation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Action Recognition with Actons | Jun Zhu, Baoyuan Wang, Xiaokang Yang, Wenjun Zhang, Zhuowen Tu | With the improved accessibility to an exploding amount of video data and growing demands in a wide range of video analysis applications, video-based action recognition/classification becomes an increasingly important task in computer vision. In this paper, we propose a two-layer structure for action recognition to auto... | 2013/pdf/Zhu_Action_Recognition_with_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhu_Action_Recognition_with_2013_ICCV_paper.pdf | @InProceedings{Zhu_2013_ICCV,author = {Zhu, Jun and Wang, Baoyuan and Yang, Xiaokang and Zhang, Wenjun and Tu, Zhuowen},title = {Action Recognition with Actons},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Exemplar Cut | Jimei Yang, Yi-Hsuan Tsai, Ming-Hsuan Yang | We present a hybrid parametric and nonparametric algorithm, exemplar cut, for generating class-specific object segmentation hypotheses. For the parametric part, we train a pylon model on a hierarchical region tree as the energy function for segmentation. For the nonparametric part, we match the input image with each ex... | 2013/pdf/Yang_Exemplar_Cut_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Yang_Exemplar_Cut_2013_ICCV_paper.pdf | @InProceedings{Yang_2013_ICCV,author = {Yang, Jimei and Tsai, Yi-Hsuan and Yang, Ming-Hsuan},title = {Exemplar Cut},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Active MAP Inference in CRFs for Efficient Semantic Segmentation | Gemma Roig, Xavier Boix, Roderick De Nijs, Sebastian Ramos, Koljia Kuhnlenz, Luc Van Gool | Most MAP inference algorithms for CRFs optimize an energy function knowing all the potentials. In this paper, we focus on CRFs where the computational cost of instantiating the potentials is orders of magnitude higher than MAP inference. This is often the case in semantic image segmentation, where most potentials are i... | 2013/pdf/Roig_Active_MAP_Inference_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Roig_Active_MAP_Inference_2013_ICCV_paper.pdf | @InProceedings{Roig_2013_ICCV,author = {Roig, Gemma and Boix, Xavier and De Nijs, Roderick and Ramos, Sebastian and Kuhnlenz, Koljia and Van Gool, Luc},title = {Active MAP Inference in CRFs for Efficient Semantic Segmentation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},mont... |
Discovering Object Functionality | Bangpeng Yao, Jiayuan Ma, Li Fei-Fei | Object functionality refers to the quality of an object that allows humans to perform some specific actions. It has been shown in psychology that functionality (affordance) is at least as essential as appearance in object recognition by humans. In computer vision, most previous work on functionality either assumes exac... | 2013/pdf/Yao_Discovering_Object_Functionality_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Yao_Discovering_Object_Functionality_2013_ICCV_paper.pdf | @InProceedings{Yao_2013_ICCV,author = {Yao, Bangpeng and Ma, Jiayuan and Fei-Fei, Li},title = {Discovering Object Functionality},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects | Stefan Duffner, Christophe Garcia | In this paper, we present a novel algorithm for fast tracking of generic objects in videos. The algorithm uses two components: a detector that makes use of the generalised Hough transform with pixel-based descriptors, and a probabilistic segmentation method based on global models for foreground and background. These co... | 2013/pdf/Duffner_PixelTrack_A_Fast_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Duffner_PixelTrack_A_Fast_2013_ICCV_paper.pdf | @InProceedings{Duffner_2013_ICCV,author = {Duffner, Stefan and Garcia, Christophe},title = {PixelTrack: A Fast Adaptive Algorithm for Tracking Non-rigid Objects},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Saliency Detection: A Boolean Map Approach | Jianming Zhang, Stan Sclaroff | A novel Boolean Map based Saliency (BMS) model is proposed. An image is characterized by a set of binary images, which are generated by randomly thresholding the image's color channels. Based on a Gestalt principle of figure-ground segregation, BMS computes saliency maps by analyzing the topological structure of Boolea... | 2013/pdf/Zhang_Saliency_Detection_A_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhang_Saliency_Detection_A_2013_ICCV_paper.pdf | @InProceedings{Zhang_2013_ICCV,author = {Zhang, Jianming and Sclaroff, Stan},title = {Saliency Detection: A Boolean Map Approach},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Class-Specific Simplex-Latent Dirichlet Allocation for Image Classification | Mandar Dixit, Nikhil Rasiwasia, Nuno Vasconcelos | An extension of the latent Dirichlet allocation (LDA), denoted class-specific-simplex LDA (css-LDA), is proposed for image classification. An analysis of the supervised LDA models currently used for this task shows that the impact of class information on the topics discovered by these models is very weak in general. Th... | 2013/pdf/Dixit_Class-Specific_Simplex-Latent_Dirichlet_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Dixit_Class-Specific_Simplex-Latent_Dirichlet_2013_ICCV_paper.pdf | @InProceedings{Dixit_2013_ICCV,author = {Dixit, Mandar and Rasiwasia, Nikhil and Vasconcelos, Nuno},title = {Class-Specific Simplex-Latent Dirichlet Allocation for Image Classification},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Bird Part Localization Using Exemplar-Based Models with Enforced Pose and Subcategory Consistency | Jiongxin Liu, Peter N. Belhumeur | In this paper, we propose a novel approach for bird part localization, targeting fine-grained categories with wide variations in appearance due to different poses (including aspect and orientation) and subcategories. As it is challenging to represent such variations across a large set of diverse samples with tractable ... | 2013/pdf/Liu_Bird_Part_Localization_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Liu_Bird_Part_Localization_2013_ICCV_paper.pdf | @InProceedings{Liu_2013_ICCV,author = {Liu, Jiongxin and Belhumeur, Peter N.},title = {Bird Part Localization Using Exemplar-Based Models with Enforced Pose and Subcategory Consistency},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
BOLD Features to Detect Texture-less Objects | Federico Tombari, Alessandro Franchi, Luigi Di Stefano | Object detection in images withstanding significant clutter and occlusion is still a challenging task whenever the object surface is characterized by poor informative content. We propose to tackle this problem by a compact and distinctive representation of groups of neighboring line segments aggregated over limited spa... | 2013/pdf/Tombari_BOLD_Features_to_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Tombari_BOLD_Features_to_2013_ICCV_paper.pdf | @InProceedings{Tombari_2013_ICCV,author = {Tombari, Federico and Franchi, Alessandro and Di Stefano, Luigi},title = {BOLD Features to Detect Texture-less Objects},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Multiple Non-rigid Surface Detection and Registration | Yi Wu, Yoshihisa Ijiri, Ming-Hsuan Yang | Detecting and registering nonrigid surfaces are two important research problems for computer vision. Much work has been done with the assumption that there exists only one instance in the image. In this work, we propose an algorithm that detects and registers multiple nonrigid instances of given objects in a cluttered ... | 2013/pdf/Wu_Multiple_Non-rigid_Surface_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Wu_Multiple_Non-rigid_Surface_2013_ICCV_paper.pdf | @InProceedings{Wu_2013_ICCV,author = {Wu, Yi and Ijiri, Yoshihisa and Yang, Ming-Hsuan},title = {Multiple Non-rigid Surface Detection and Registration},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Drosophila Embryo Stage Annotation Using Label Propagation | Tomas Kazmar, Evgeny Z. Kvon, Alexander Stark, Christoph H. Lampert | In this work we propose a system for automatic classification of Drosophila embryos into developmental stages. While the system is designed to solve an actual problem in biological research, we believe that the principle underlying it is interesting not only for biologists, but also for researchers in computer vision. ... | 2013/pdf/Kazmar_Drosophila_Embryo_Stage_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Kazmar_Drosophila_Embryo_Stage_2013_ICCV_paper.pdf | @InProceedings{Kazmar_2013_ICCV,author = {Kazmar, Tomas and Kvon, Evgeny Z. and Stark, Alexander and Lampert, Christoph H.},title = {Drosophila Embryo Stage Annotation Using Label Propagation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Parsing IKEA Objects: Fine Pose Estimation | Joseph J. Lim, Hamed Pirsiavash, Antonio Torralba | We address the problem of localizing and estimating the fine-pose of objects in the image with exact 3D models. Our main focus is to unify contributions from the 1970s with recent advances in object detection: use local keypoint detectors to find candidate poses and score global alignment of each candidate pose to the ... | 2013/pdf/Lim_Parsing_IKEA_Objects_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Lim_Parsing_IKEA_Objects_2013_ICCV_paper.pdf | @InProceedings{Lim_2013_ICCV,author = {Lim, Joseph J. and Pirsiavash, Hamed and Torralba, Antonio},title = {Parsing IKEA Objects: Fine Pose Estimation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Corrected-Moment Illuminant Estimation | Graham D. Finlayson | Image colors are biased by the color of the prevaling illumination. As such the color at pixel cannot always be used directly in solving vision tasks from recognition, to tracking to general scene understanding. Illuminant estimation algorithms attempt to infer the color of the light incident in a scene and then a colo... | 2013/pdf/Finlayson_Corrected-Moment_Illuminant_Estimation_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Finlayson_Corrected-Moment_Illuminant_Estimation_2013_ICCV_paper.pdf | @InProceedings{Finlayson_2013_ICCV,author = {Finlayson, Graham D.},title = {Corrected-Moment Illuminant Estimation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps | Jiajia Luo, Wei Wang, Hairong Qi | Human action recognition based on the depth information provided by commodity depth sensors is an important yet challenging task. The noisy depth maps, different lengths of action sequences, and free styles in performing actions, may cause large intra-class variations. In this paper, a new framework based on sparse cod... | 2013/pdf/Luo_Group_Sparsity_and_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Luo_Group_Sparsity_and_2013_ICCV_paper.pdf | @InProceedings{Luo_2013_ICCV,author = {Luo, Jiajia and Wang, Wei and Qi, Hairong},title = {Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Online Video SEEDS for Temporal Window Objectness | Michael Van Den Bergh, Gemma Roig, Xavier Boix, Santiago Manen, Luc Van Gool | Superpixel and objectness algorithms are broadly used as a pre-processing step to generate support regions and to speed-up further computations. Recently, many algorithms have been extended to video in order to exploit the temporal consistency between frames. However, most methods are computationally too expensive for ... | 2013/pdf/Van_Den_Bergh_Online_Video_SEEDS_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Van_Den_Bergh_Online_Video_SEEDS_2013_ICCV_paper.pdf | @InProceedings{Bergh_2013_ICCV,author = {Van Den Bergh, Michael and Roig, Gemma and Boix, Xavier and Manen, Santiago and Van Gool, Luc},title = {Online Video SEEDS for Temporal Window Objectness},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Fast Subspace Search via Grassmannian Based Hashing | Xu Wang, Stefan Atev, John Wright, Gilad Lerman | The problem of efficiently deciding which of a database of models is most similar to a given input query arises throughout modern computer vision. Motivated by applications in recognition, image retrieval and optimization, there has been significant recent interest in the variant of this problem in which the database m... | 2013/pdf/Wang_Fast_Subspace_Search_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Wang_Fast_Subspace_Search_2013_ICCV_paper.pdf | @InProceedings{Wang_2013_ICCV,author = {Wang, Xu and Atev, Stefan and Wright, John and Lerman, Gilad},title = {Fast Subspace Search via Grassmannian Based Hashing},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Data-Driven 3D Primitives for Single Image Understanding | David F. Fouhey, Abhinav Gupta, Martial Hebert | What primitives should we use to infer the rich 3D world behind an image? We argue that these primitives should be both visually discriminative and geometrically informative and we present a technique for discovering such primitives. We demonstrate the utility of our primitives by using them to infer 3D surface normals... | 2013/pdf/Fouhey_Data-Driven_3D_Primitives_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Fouhey_Data-Driven_3D_Primitives_2013_ICCV_paper.pdf | @InProceedings{Fouhey_2013_ICCV,author = {Fouhey, David F. and Gupta, Abhinav and Hebert, Martial},title = {Data-Driven 3D Primitives for Single Image Understanding},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Fast Face Detector Training Using Tailored Views | Kristina Scherbaum, James Petterson, Rogerio S. Feris, Volker Blanz, Hans-Peter Seidel | Face detection is an important task in computer vision and often serves as the first step for a variety of applications. State-of-the-art approaches use efficient learning algorithms and train on large amounts of manually labeled imagery. Acquiring appropriate training images, however, is very time-consuming and does n... | 2013/pdf/Scherbaum_Fast_Face_Detector_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Scherbaum_Fast_Face_Detector_2013_ICCV_paper.pdf | @InProceedings{Scherbaum_2013_ICCV,author = {Scherbaum, Kristina and Petterson, James and Feris, Rogerio S. and Blanz, Volker and Seidel, Hans-Peter},title = {Fast Face Detector Training Using Tailored Views},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},yea... |
Partial Enumeration and Curvature Regularization | Carl Olsson, Johannes Ulen, Yuri Boykov, Vladimir Kolmogorov | Energies with high-order non-submodular interactions have been shown to be very useful in vision due to their high modeling power. Optimization of such energies, however, is generally NP-hard. A naive approach that works for small problem instances is exhaustive search, that is, enumeration of all possible labelings of... | 2013/pdf/Olsson_Partial_Enumeration_and_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Olsson_Partial_Enumeration_and_2013_ICCV_paper.pdf | @InProceedings{Olsson_2013_ICCV,author = {Olsson, Carl and Ulen, Johannes and Boykov, Yuri and Kolmogorov, Vladimir},title = {Partial Enumeration and Curvature Regularization},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Image Retrieval Using Textual Cues | Anand Mishra, Karteek Alahari, C.V. Jawahar | We present an approach for the text-to-image retrieval problem based on textual content present in images. Given the recent developments in understanding text in images, an appealing approach to address this problem is to localize and recognize the text, and then query the database, as in a text retrieval problem. We s... | 2013/pdf/Mishra_Image_Retrieval_Using_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Mishra_Image_Retrieval_Using_2013_ICCV_paper.pdf | @InProceedings{Mishra_2013_ICCV,author = {Mishra, Anand and Alahari, Karteek and Jawahar, C.V.},title = {Image Retrieval Using Textual Cues},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Fluttering Pattern Generation Using Modified Legendre Sequence for Coded Exposure Imaging | Hae-Gon Jeon, Joon-Young Lee, Yudeog Han, Seon Joo Kim, In So Kweon | Finding a good binary sequence is critical in determining the performance of the coded exposure imaging, but previous methods mostly rely on a random search for finding the binary codes, which could easily fail to find good long sequences due to the exponentially growing search space. In this paper, we present a new co... | 2013/pdf/Jeon_Fluttering_Pattern_Generation_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Jeon_Fluttering_Pattern_Generation_2013_ICCV_paper.pdf | @InProceedings{Jeon_2013_ICCV,author = {Jeon, Hae-Gon and Lee, Joon-Young and Han, Yudeog and Kim, Seon Joo and Kweon, In So},title = {Fluttering Pattern Generation Using Modified Legendre Sequence for Coded Exposure Imaging},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month... |
Prime Object Proposals with Randomized Prim's Algorithm | Santiago Manen, Matthieu Guillaumin, Luc Van Gool | Generic object detection is the challenging task of proposing windows that localize all the objects in an image, regardless of their classes. Such detectors have recently been shown to benefit many applications such as speedingup class-specific object detection, weakly supervised learning of object detectors and object... | 2013/pdf/Manen_Prime_Object_Proposals_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Manen_Prime_Object_Proposals_2013_ICCV_paper.pdf | @InProceedings{Manen_2013_ICCV,author = {Manen, Santiago and Guillaumin, Matthieu and Van Gool, Luc},title = {Prime Object Proposals with Randomized Prim's Algorithm},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Semi-supervised Robust Dictionary Learning via Efficient l-Norms Minimization | Hua Wang, Feiping Nie, Weidong Cai, Heng Huang | Representing the raw input of a data set by a set of relevant codes is crucial to many computer vision applications. Due to the intrinsic sparse property of real-world data, dictionary learning, in which the linear decomposition of a data point uses a set of learned dictionary bases, i.e., codes, has demonstrated state... | 2013/pdf/Wang_Semi-supervised_Robust_Dictionary_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Wang_Semi-supervised_Robust_Dictionary_2013_ICCV_paper.pdf | @InProceedings{Wang_2013_ICCV,author = {Wang, Hua and Nie, Feiping and Cai, Weidong and Huang, Heng},title = {Semi-supervised Robust Dictionary Learning via Efficient l-Norms Minimization},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Optimization Problems for Fast AAM Fitting in-the-Wild | Georgios Tzimiropoulos, Maja Pantic | We describe a very simple framework for deriving the most-well known optimization problems in Active Appearance Models (AAMs), and most importantly for providing efficient solutions. Our formulation results in two optimization problems for fast and exact AAM fitting, and one new algorithm which has the important advant... | 2013/pdf/Tzimiropoulos_Optimization_Problems_for_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Tzimiropoulos_Optimization_Problems_for_2013_ICCV_paper.pdf | @InProceedings{Tzimiropoulos_2013_ICCV,author = {Tzimiropoulos, Georgios and Pantic, Maja},title = {Optimization Problems for Fast AAM Fitting in-the-Wild},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Cosegmentation and Cosketch by Unsupervised Learning | Jifeng Dai, Ying Nian Wu, Jie Zhou, Song-Chun Zhu | Cosegmentation refers to the problem of segmenting multiple images simultaneously by exploiting the similarities between the foreground and background regions in these images. The key issue in cosegmentation is to align common objects between these images. To address this issue, we propose an unsupervised learning fram... | 2013/pdf/Dai_Cosegmentation_and_Cosketch_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Dai_Cosegmentation_and_Cosketch_2013_ICCV_paper.pdf | @InProceedings{Dai_2013_ICCV,author = {Dai, Jifeng and Wu, Ying Nian and Zhou, Jie and Zhu, Song-Chun},title = {Cosegmentation and Cosketch by Unsupervised Learning},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Joint Learning of Discriminative Prototypes and Large Margin Nearest Neighbor Classifiers | Martin Kostinger, Paul Wohlhart, Peter M. Roth, Horst Bischof | In this paper, we raise important issues concerning the evaluation complexity of existing Mahalanobis metric learning methods. The complexity scales linearly with the size of the dataset. This is especially cumbersome on large scale or for real-time applications with limited time budget. To alleviate this problem we pr... | 2013/pdf/Kostinger_Joint_Learning_of_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Kostinger_Joint_Learning_of_2013_ICCV_paper.pdf | @InProceedings{Kostinger_2013_ICCV,author = {Kostinger, Martin and Wohlhart, Paul and Roth, Peter M. and Bischof, Horst},title = {Joint Learning of Discriminative Prototypes and Large Margin Nearest Neighbor Classifiers},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {D... |
Joint Optimization for Consistent Multiple Graph Matching | Junchi Yan, Yu Tian, Hongyuan Zha, Xiaokang Yang, Ya Zhang, Stephen M. Chu | The problem of graph matching in general is NP-hard and approaches have been proposed for its suboptimal solution, most focusing on finding the one-to-one node mapping between two graphs. A more general and challenging problem arises when one aims to find consistent mappings across a number of graphs more than two. Con... | 2013/pdf/Yan_Joint_Optimization_for_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Yan_Joint_Optimization_for_2013_ICCV_paper.pdf | @InProceedings{Yan_2013_ICCV,author = {Yan, Junchi and Tian, Yu and Zha, Hongyuan and Yang, Xiaokang and Zhang, Ya and Chu, Stephen M.},title = {Joint Optimization for Consistent Multiple Graph Matching},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {... |
Scene Collaging: Analysis and Synthesis of Natural Images with Semantic Layers | Phillip Isola, Ce Liu | To quickly synthesize complex scenes, digital artists often collage together visual elements from multiple sources: for example, mountains from New Zealand behind a Scottish castle with wisps of Saharan sand in front. In this paper, we propose to use a similar process in order to parse a scene. We model a scene as a co... | 2013/pdf/Isola_Scene_Collaging_Analysis_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Isola_Scene_Collaging_Analysis_2013_ICCV_paper.pdf | @InProceedings{Isola_2013_ICCV,author = {Isola, Phillip and Liu, Ce},title = {Scene Collaging: Analysis and Synthesis of Natural Images with Semantic Layers},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Quadruplet-Wise Image Similarity Learning | Marc T. Law, Nicolas Thome, Matthieu Cord | This paper introduces a novel similarity learning framework. Working with inequality constraints involving quadruplets of images, our approach aims at efficiently modeling similarity from rich or complex semantic label relationships. From these quadruplet-wise constraints, we propose a similarity learning framework rel... | 2013/pdf/Law_Quadruplet-Wise_Image_Similarity_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Law_Quadruplet-Wise_Image_Similarity_2013_ICCV_paper.pdf | @InProceedings{Law_2013_ICCV,author = {Law, Marc T. and Thome, Nicolas and Cord, Matthieu},title = {Quadruplet-Wise Image Similarity Learning},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Facial Action Unit Event Detection by Cascade of Tasks | Xiaoyu Ding, Wen-Sheng Chu, Fernando De La Torre, Jeffery F. Cohn, Qiao Wang | Automatic facial Action Unit (AU) detection from video is a long-standing problem in facial expression analysis. AU detection is typically posed as a classification problem between frames or segments of positive examples and negative ones, where existing work emphasizes the use of different features or classifiers. In ... | 2013/pdf/Ding_Facial_Action_Unit_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Ding_Facial_Action_Unit_2013_ICCV_paper.pdf | @InProceedings{Ding_2013_ICCV,author = {Ding, Xiaoyu and Chu, Wen-Sheng and De La Torre, Fernando and Cohn, Jeffery F. and Wang, Qiao},title = {Facial Action Unit Event Detection by Cascade of Tasks},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013... |
Cascaded Shape Space Pruning for Robust Facial Landmark Detection | Xiaowei Zhao, Shiguang Shan, Xiujuan Chai, Xilin Chen | In this paper, we propose a novel cascaded face shape space pruning algorithm for robust facial landmark detection. Through progressively excluding the incorrect candidate shapes, our algorithm can accurately and efficiently achieve the globally optimal shape configuration. Specifically, individual landmark detectors a... | 2013/pdf/Zhao_Cascaded_Shape_Space_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhao_Cascaded_Shape_Space_2013_ICCV_paper.pdf | @InProceedings{Zhao_2013_ICCV,author = {Zhao, Xiaowei and Shan, Shiguang and Chai, Xiujuan and Chen, Xilin},title = {Cascaded Shape Space Pruning for Robust Facial Landmark Detection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Efficient Higher-Order Clustering on the Grassmann Manifold | Suraj Jain, Venu Madhav Govindu | The higher-order clustering problem arises when data is drawn from multiple subspaces or when observations fit a higher-order parametric model. Most solutions to this problem either decompose higher-order similarity measures for use in spectral clustering or explicitly use low-rank matrix representations. In this paper... | 2013/pdf/Jain_Efficient_Higher-Order_Clustering_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Jain_Efficient_Higher-Order_Clustering_2013_ICCV_paper.pdf | @InProceedings{Jain_2013_ICCV,author = {Jain, Suraj and Govindu, Venu Madhav},title = {Efficient Higher-Order Clustering on the Grassmann Manifold},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction | Ning Zhang, Ryan Farrell, Forrest Iandola, Trevor Darrell | Recognizing objects in fine-grained domains can be extremely challenging due to the subtle differences between subcategories. Discriminative markings are often highly localized, leading traditional object recognition approaches to struggle with the large pose variation often present in these domains. Pose-normalization... | 2013/pdf/Zhang_Deformable_Part_Descriptors_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhang_Deformable_Part_Descriptors_2013_ICCV_paper.pdf | @InProceedings{Zhang_2013_ICCV,author = {Zhang, Ning and Farrell, Ryan and Iandola, Forrest and Darrell, Trevor},title = {Deformable Part Descriptors for Fine-Grained Recognition and Attribute Prediction},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = ... |
Compensating for Motion during Direct-Global Separation | Supreeth Achar, Stephen T. Nuske, Srinivasa G. Narasimhan | Separating the direct and global components of radiance can aid shape recovery algorithms and can provide useful information about materials in a scene. Practical methods for finding the direct and global components use multiple images captured under varying illumination patterns and require the scene, light source and... | 2013/pdf/Achar_Compensating_for_Motion_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Achar_Compensating_for_Motion_2013_ICCV_paper.pdf | @InProceedings{Achar_2013_ICCV,author = {Achar, Supreeth and Nuske, Stephen T. and Narasimhan, Srinivasa G.},title = {Compensating for Motion during Direct-Global Separation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
A Scalable Unsupervised Feature Merging Approach to Efficient Dimensionality Reduction of High-Dimensional Visual Data | Lingqiao Liu, Lei Wang | To achieve a good trade-off between recognition accuracy and computational efficiency, it is often needed to reduce high-dimensional visual data to medium-dimensional ones. For this task, even applying a simple full-matrixbased linear projection causes significant computation and memory use. When the number of visual d... | 2013/pdf/Liu_A_Scalable_Unsupervised_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Liu_A_Scalable_Unsupervised_2013_ICCV_paper.pdf | @InProceedings{Liu_2013_ICCV,author = {Liu, Lingqiao and Wang, Lei},title = {A Scalable Unsupervised Feature Merging Approach to Efficient Dimensionality Reduction of High-Dimensional Visual Data},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Shufflets: Shared Mid-level Parts for Fast Object Detection | Iasonas Kokkinos | We present a method to identify and exploit structures that are shared across different object categories, by using sparse coding to learn a shared basis for the 'part' and 'root' templates of Deformable Part Models (DPMs). Our first contribution consists in using Shift-Invariant Sparse Coding (SISC) to learn mid-level... | 2013/pdf/Kokkinos_Shufflets_Shared_Mid-level_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Kokkinos_Shufflets_Shared_Mid-level_2013_ICCV_paper.pdf | @InProceedings{Kokkinos_2013_ICCV,author = {Kokkinos, Iasonas},title = {Shufflets: Shared Mid-level Parts for Fast Object Detection},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
GrabCut in One Cut | Meng Tang, Lena Gorelick, Olga Veksler, Yuri Boykov | Among image segmentation algorithms there are two major groups: (a) methods assuming known appearance models and (b) methods estimating appearance models jointly with segmentation. Typically, the first group optimizes appearance log-likelihoods in combination with some spacial regularization. This problem is relatively... | 2013/pdf/Tang_GrabCut_in_One_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Tang_GrabCut_in_One_2013_ICCV_paper.pdf | @InProceedings{Tang_2013_ICCV,author = {Tang, Meng and Gorelick, Lena and Veksler, Olga and Boykov, Yuri},title = {GrabCut in One Cut},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Coupling Alignments with Recognition for Still-to-Video Face Recognition | Zhiwu Huang, Xiaowei Zhao, Shiguang Shan, Ruiping Wang, Xilin Chen | The Still-to-Video (S2V) face recognition systems typically need to match faces in low-quality videos captured under unconstrained conditions against high quality still face images, which is very challenging because of noise, image blur, low face resolutions, varying head pose, complex lighting, and alignment difficult... | 2013/pdf/Huang_Coupling_Alignments_with_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Huang_Coupling_Alignments_with_2013_ICCV_paper.pdf | @InProceedings{Huang_2013_ICCV,author = {Huang, Zhiwu and Zhao, Xiaowei and Shan, Shiguang and Wang, Ruiping and Chen, Xilin},title = {Coupling Alignments with Recognition for Still-to-Video Face Recognition},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},yea... |
Query-Adaptive Asymmetrical Dissimilarities for Visual Object Retrieval | Cai-Zhi Zhu, Herve Jegou, Shin Ichi Satoh | Visual object retrieval aims at retrieving, from a collection of images, all those in which a given query object appears. It is inherently asymmetric: the query object is mostly included in the database image, while the converse is not necessarily true. However, existing approaches mostly compare the images with symmet... | 2013/pdf/Zhu_Query-Adaptive_Asymmetrical_Dissimilarities_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Zhu_Query-Adaptive_Asymmetrical_Dissimilarities_2013_ICCV_paper.pdf | @InProceedings{Zhu_2013_ICCV,author = {Zhu, Cai-Zhi and Jegou, Herve and Satoh, Shin Ichi},title = {Query-Adaptive Asymmetrical Dissimilarities for Visual Object Retrieval},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology | Hang Chang, Yin Zhou, Paul Spellman, Bahram Parvin | Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), provides a series of indices for tumor composition. Furthermore, aggregation of these indices, from each whole slide image (WSI) in a large cohort, can provide predictive models of the clinical outcome. Howe... | 2013/pdf/Chang_Stacked_Predictive_Sparse_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Chang_Stacked_Predictive_Sparse_2013_ICCV_paper.pdf | @InProceedings{Chang_2013_ICCV,author = {Chang, Hang and Zhou, Yin and Spellman, Paul and Parvin, Bahram},title = {Stacked Predictive Sparse Coding for Classification of Distinct Regions in Tumor Histopathology},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},... |
Direct Optimization of Frame-to-Frame Rotation | Laurent Kneip, Simon Lynen | This work makes use of a novel, recently proposed epipolar constraint for computing the relative pose between two calibrated images. By enforcing the coplanarity of epipolar plane normal vectors, it constrains the three degrees of freedom of the relative rotation between two camera views directly--independently of the ... | 2013/pdf/Kneip_Direct_Optimization_of_2013_ICCV_paper.pdf | https://openaccess.thecvf.com/content_iccv_2013/papers/Kneip_Direct_Optimization_of_2013_ICCV_paper.pdf | @InProceedings{Kneip_2013_ICCV,author = {Kneip, Laurent and Lynen, Simon},title = {Direct Optimization of Frame-to-Frame Rotation},booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},month = {December},year = {2013}} |
End of preview. Expand in Data Studio
ICCV Papers
ICCV (International Conference on Computer Vision) is one of the most prestigious conferences in computer vision, held biennially since 1987. Along with CVPR and ECCV, it forms the top-tier venues for computer vision research. ICCV papers have contributed groundbreaking work in areas such as object detection, image segmentation, 3D reconstruction, and video understanding.
ICCV Papers is a comprehensive dataset containing all papers from ICCV 2013 to present, including metadata and PDF download links. It is designed for literature review, trend analysis, citation network construction, and various research tasks in computer vision.
Pipeline
The dataset is constructed through a systematic multi-stage pipeline:
- Web Scraping: Extract paper listings from CVF Open Access repository
- Metadata Extraction: Parse HTML to extract titles, authors, PDF links, and BibTeX citations
- Abstract Retrieval: Fetch abstracts from individual paper detail pages
- URL Generation: Generate direct download URLs for PDFs
- Data Validation: Verify data integrity and format consistency
Dataset Structure
ICCV_Papers/
├── 2013/
│ ├── pdf/ # PDF files for all papers
│ │ ├── paper1.pdf
│ │ ├── paper2.pdf
│ │ └── ...
│ └── meta.jsonl # Metadata
├── 2015/
│ ├── pdf/
│ └── meta.jsonl
├── 2017/
│ ├── pdf/
│ └── meta.jsonl
└── ...
Dataset Overview
- Total Papers: 9,145 (ICCV 2013-2025, continuously expanding)
- Data Format: JSONL for metadata, PDF for full papers, organized by year
- Source: CVF Open Access
| Field | Type | Description |
|---|---|---|
title |
string | Paper title |
authors |
string | Comma-separated list of authors |
abstract |
string | Paper abstract |
pdf_path |
string | Relative path to PDF file |
download_url |
string | Direct download URL for PDF |
bibtex |
string | BibTeX citation string |
Example Entry:
{
"title": "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation",
"authors": "Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik",
"abstract": "Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years...",
"pdf_path": "2014/pdf/Girshick_Rich_Feature_2014_CVPR_paper.pdf",
"download_url": "https://openaccess.thecvf.com/content_cvpr_2014/papers/Girshick_Rich_Feature_2014_CVPR_paper.pdf",
"bibtex": "@InProceedings{Girshick_2014_CVPR,author = {Girshick, Ross and Donahue, Jeff and Darrell, Trevor and Malik, Jitendra},title = {Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation},booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},month = {June},year = {2014}}"
}
Quick Start
Installation
pip install huggingface_hub requests
Load Metadata
from huggingface_hub import hf_hub_download
import json
# Download metadata for a specific year
year = "2013"
meta_path = hf_hub_download(
repo_id="choucsan/ICCV_Papers",
filename=f"{year}/meta.jsonl",
repo_type="dataset",
)
# Load metadata
papers = []
with open(meta_path, 'r', encoding='utf-8') as f:
for line in f:
papers.append(json.loads(line))
print(f"Loaded {len(papers)} ICCV {year} papers")
Download PDF Files
Each paper has a download_url field pointing to the original PDF on CVF Open Access:
import requests
import os
# Create output directory
os.makedirs(f"iccv_{year}_pdfs", exist_ok=True)
# Download a specific paper
paper = papers[0]
response = requests.get(paper['download_url'])
filename = os.path.basename(paper['pdf_path'])
with open(f"iccv_{year}_pdfs/{filename}", 'wb') as f:
f.write(response.content)
print(f"Downloaded: {filename}")
# Download all papers for a year (optional)
for paper in papers:
if paper.get('download_url'):
response = requests.get(paper['download_url'])
filename = os.path.basename(paper['pdf_path'])
with open(f"iccv_{year}_pdfs/{filename}", 'wb') as f:
f.write(response.content)
Applications
PDF Access
- Direct Download: Use
download_urlto download PDFs directly from CVF Open Access - Full Text Analysis: Extract text from PDFs for detailed content analysis
- Figure Extraction: Extract figures, tables, and equations from papers
- Layout Analysis: Analyze paper structure and formatting patterns
- OCR Processing: Optical character recognition for scanned documents
Research Applications
- Literature Review: Rapid retrieval of papers in specific domains for comprehensive literature review
- Trend Analysis: Analyze research hotspots and development trends in computer vision over the past decade
- Citation Networks: Build and analyze paper citation networks based on BibTeX data
- Knowledge Graphs: Construct knowledge graphs connecting papers, authors, institutions, and concepts
- Recommendation Systems: Build paper recommendation systems based on content similarity
Download
- Hugging Face: datasets/choucsan/ICCV_Papers
Contact
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