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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- behavior |
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- motion |
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- human |
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- egocentric |
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- language |
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- llm |
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- vlm |
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- esk |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- question-answering |
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--- |
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# 🍋 EPFL-Smart-Kitchen: Lemonade benchmark |
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## 📚 Introduction |
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we introduce Lemonade: **L**anguage models **E**valuation of **MO**tion a**N**d **A**ction-**D**riven **E**nquiries. |
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Lemonade consists of <span style="color: orange;">36,521</span> closed-ended QA pairs linked to egocentric video clips, categorized in three groups and six subcategories. <span style="color: orange;">18,857</span> QAs focus on behavior understanding, leveraging the rich ground truth behavior annotations of the EPFL-Smart Kitchen to interrogate models about perceived actions <span style="color: tomato;">(Perception)</span> and reason over unseen behaviors <span style="color: tomato;">(Reasoning)</span>. <span style="color: orange;">8,210</span> QAs involve longer video clips, challenging models in summarization <span style="color: gold;">(Summarization)</span> and session-level inference <span style="color: gold;">(Session properties)</span>. The remaining <span style="color: orange;">9,463</span> QAs leverage the 3D pose estimation data to infer hand shapes, joint angles <span style="color: skyblue;">(Physical attributes)</span>, or trajectory velocities <span style="color: skyblue;">(Kinematics)</span> from visual information. |
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## 💾 Content |
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The current repository contains all egocentric videos recorded in the EPFL-Smart-Kitchen-30 dataset and the question answer pairs of the Lemonade benchmark. Please refer to the [main GitHub repository](https://github.com/amathislab/EPFL-Smart-Kitchen#) to find the other benchmarks and links to download other modalities of the EPFL-Smart-Kitchen-30 dataset. |
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### 🗃️ Repository structure |
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``` |
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Lemonade |
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├── MCQs |
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| └── lemonade_benchmark.csv |
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├── videos |
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| ├── YH2002_2023_12_04_10_15_23_hololens.mp4 |
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| └── .. |
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└── README.md |
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``` |
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`lemonade_benchmark.csv` : Table with the following fields: |
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**Question** : Question to be answered. </br> |
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**QID** : Question identifier, an integer from 0 to 30. </br> |
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**Answers** : A list of possible answers to the question. This can be a multiple-choice set or open-ended responses. </br> |
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**Correct Answer** : The answer that is deemed correct from the list of provided answers. </br> |
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**Clip** : A reference to the video clip related to the question. </br> |
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**Start** : The timestamp (in frames) in the clip where the question context begins. </br> |
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**End** : The timestamp (in frames) in the clip where the question context ends. </br> |
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**Category** : The broad topic under which the question falls (Behavior understanding, Long-term understanding or Motion and Biomechanics). </br> |
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**Subcategory** : A more refined classification within the category (Perception, Reasoning, Summarization, Session properties, Physical attributes, Kinematics). </br> |
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**Difficulty** : The complexity level of the question (e.g., Easy, Medium, Hard). |
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`videos` : Folder with all egocentric videos from the EPFL-Smart-Kitchen-30 benchmark. Video names are structured as `[Participant_ID]_[Session_name]_hololens.mp4`. |
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> We refer the reader to the associated publication for details about data processing and tasks description. |
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## 📈 Evaluation results |
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## 🌈 Usage |
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The evaluation of the benchmark can be done through the following github repository: [https://github.com/amathislab/lmms-eval-lemonade](https://github.com/amathislab/lmms-eval-lemonade) |
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## 🌟 Citations |
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Please cite our work! |
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``` |
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@misc{bonnetto2025epflsmartkitchen, |
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title={EPFL-Smart-Kitchen-30: Densely annotated cooking dataset with 3D kinematics to challenge video and language models}, |
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author={Andy Bonnetto and Haozhe Qi and Franklin Leong and Matea Tashkovska and Mahdi Rad and Solaiman Shokur and Friedhelm Hummel and Silvestro Micera and Marc Pollefeys and Alexander Mathis}, |
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year={2025}, |
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eprint={2506.01608}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2506.01608}, |
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} |
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``` |
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## ❤️ Acknowledgments |
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Our work was funded by EPFL, Swiss SNF grant (320030-227871), Microsoft Swiss Joint Research Center and a Boehringer Ingelheim Fonds PhD stipend (H.Q.). We are grateful to the Brain Mind Institute for providing funds for hardware and to the Neuro-X Institute for providing funds for services. |
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