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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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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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