Analyzing The Language of Visual Tokens
Paper
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2411.05001
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Published
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24
Multimodal Analysis
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Large Multi-modal Models Can Interpret Features in Large Multi-modal Models
Paper • 2411.14982 • Published • 19 -
Rethinking Token Reduction in MLLMs: Towards a Unified Paradigm for Training-Free Acceleration
Paper • 2411.17686 • Published • 20 -
On the Limitations of Vision-Language Models in Understanding Image Transforms
Paper • 2503.09837 • Published • 10 -
Multimodal Chain-of-Thought Reasoning: A Comprehensive Survey
Paper • 2503.12605 • Published • 35 -
When Less is Enough: Adaptive Token Reduction for Efficient Image Representation
Paper • 2503.16660 • Published • 72 -
From Head to Tail: Towards Balanced Representation in Large Vision-Language Models through Adaptive Data Calibration
Paper • 2503.12821 • Published • 9 -
Scaling Laws for Native Multimodal Models Scaling Laws for Native Multimodal Models
Paper • 2504.07951 • Published • 29 -
Textual Steering Vectors Can Improve Visual Understanding in Multimodal Large Language Models
Paper • 2505.14071 • Published • 1 -
MLLMs are Deeply Affected by Modality Bias
Paper • 2505.18657 • Published • 5 -
To Trust Or Not To Trust Your Vision-Language Model's Prediction
Paper • 2505.23745 • Published • 4 -
Vision Language Models are Biased
Paper • 2505.23941 • Published • 23 -
Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning
Paper • 2506.04755 • Published • 37 -
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand Better
Paper • 2506.09040 • Published • 34 -
How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks
Paper • 2507.01955 • Published • 35 -
Robust Multimodal Large Language Models Against Modality Conflict
Paper • 2507.07151 • Published • 5 -
Automating Steering for Safe Multimodal Large Language Models
Paper • 2507.13255 • Published • 3 -
When Tokens Talk Too Much: A Survey of Multimodal Long-Context Token Compression across Images, Videos, and Audios
Paper • 2507.20198 • Published • 26 -
Adapting Vision-Language Models Without Labels: A Comprehensive Survey
Paper • 2508.05547 • Published • 11 -
Enhancing Vision-Language Model Training with Reinforcement Learning in Synthetic Worlds for Real-World Success
Paper • 2508.04280 • Published • 35 -
Controlling Multimodal LLMs via Reward-guided Decoding
Paper • 2508.11616 • Published • 7 -
IAG: Input-aware Backdoor Attack on VLMs for Visual Grounding
Paper • 2508.09456 • Published • 8 -
MMTok: Multimodal Coverage Maximization for Efficient Inference of VLMs
Paper • 2508.18264 • Published • 25 -
Visual Representation Alignment for Multimodal Large Language Models
Paper • 2509.07979 • Published • 82 -
Lost in Embeddings: Information Loss in Vision-Language Models
Paper • 2509.11986 • Published • 27 -
LLM-I: LLMs are Naturally Interleaved Multimodal Creators
Paper • 2509.13642 • Published • 8 -
When Big Models Train Small Ones: Label-Free Model Parity Alignment for Efficient Visual Question Answering using Small VLMs
Paper • 2509.16633 • Published • 1 -
Where MLLMs Attend and What They Rely On: Explaining Autoregressive Token Generation
Paper • 2509.22496 • Published • 3 -
Visual Jigsaw Post-Training Improves MLLMs
Paper • 2509.25190 • Published • 35 -
On Epistemic Uncertainty of Visual Tokens for Object Hallucinations in Large Vision-Language Models
Paper • 2510.09008 • Published • 14 -
RL makes MLLMs see better than SFT
Paper • 2510.16333 • Published • 43