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--- |
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license: mit |
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extra_gated_prompt: >- |
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You agree to not use the dataset to conduct experiments that cause harm to |
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human subjects. Please note that the data in this dataset may be subject to |
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other agreements. Before using the data, be sure to read the relevant |
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agreements carefully to ensure compliant use. Video copyrights belong to the |
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original video creators or platforms and are for academic research use only. |
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task_categories: |
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- visual-question-answering |
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- video-classification |
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extra_gated_fields: |
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Name: text |
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Company/Organization: text |
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Country: text |
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E-Mail: text |
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modalities: |
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- Video |
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- Text |
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configs: |
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- config_name: action_sequence |
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data_files: json/action_sequence.json |
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- config_name: moving_count |
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data_files: json/moving_count.json |
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- config_name: action_prediction |
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data_files: json/action_prediction.json |
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- config_name: episodic_reasoning |
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data_files: json/episodic_reasoning.json |
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- config_name: action_antonym |
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data_files: json/action_antonym.json |
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- config_name: action_count |
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data_files: json/action_count.json |
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- config_name: scene_transition |
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data_files: json/scene_transition.json |
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- config_name: object_shuffle |
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data_files: json/object_shuffle.json |
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- config_name: object_existence |
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data_files: json/object_existence.json |
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- config_name: fine_grained_pose |
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data_files: json/fine_grained_pose.json |
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- config_name: unexpected_action |
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data_files: json/unexpected_action.json |
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- config_name: moving_direction |
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data_files: json/moving_direction.json |
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- config_name: state_change |
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data_files: json/state_change.json |
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- config_name: object_interaction |
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data_files: json/object_interaction.json |
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- config_name: character_order |
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data_files: json/character_order.json |
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- config_name: action_localization |
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data_files: json/action_localization.json |
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- config_name: counterfactual_inference |
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data_files: json/counterfactual_inference.json |
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- config_name: fine_grained_action |
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data_files: json/fine_grained_action.json |
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- config_name: moving_attribute |
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data_files: json/moving_attribute.json |
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- config_name: egocentric_navigation |
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data_files: json/egocentric_navigation.json |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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--- |
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# MVBench |
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## Dataset Description |
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- **Repository:** [MVBench](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb) |
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- **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005) |
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- **Point of Contact:** mailto:[kunchang li](likunchang@pjlab.org.cn) |
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## <span style="color: red;">Important Update</span> |
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[18/10/2024] Due to NTU RGB+D License, 320 videos from NTU RGB+D need to be downloaded manually. Please visit [ROSE Lab](https://rose1.ntu.edu.sg/dataset/actionRecognition/) to access the data. We also provide a [list of the 320 videos](https://huggingface.co/datasets/OpenGVLab/MVBench/blob/main/video/MVBench_videos_ntu.txt) used in MVBench for your reference. |
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We introduce a novel static-to-dynamic method for defining temporal-related tasks. By converting static tasks into dynamic ones, we facilitate systematic generation of video tasks necessitating a wide range of temporal abilities, from perception to cognition. Guided by task definitions, we then **automatically transform public video annotations into multiple-choice QA** for task evaluation. This unique paradigm enables efficient creation of MVBench with minimal manual intervention while ensuring evaluation fairness through ground-truth video annotations and avoiding biased LLM scoring. The **20** temporal task examples are as follows. |
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## Evaluation |
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An evaluation example is provided in [mvbench.ipynb](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb). Please follow the pipeline to prepare the evaluation code for various MLLMs. |
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- **Preprocess**: We preserve the raw video (high resolution, long duration, etc.) along with corresponding annotations (start, end, subtitles, etc.) for future exploration; hence, the decoding of some raw videos like Perception Test may be slow. |
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- **Prompt**: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction. |
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## Leadrboard |
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While an [Online leaderboard]() is under construction, the current standings are as follows: |
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