| --- |
| license: mit |
| extra_gated_prompt: >- |
| You agree to not use the dataset to conduct experiments that cause harm to |
| human subjects. Please note that the data in this dataset may be subject to |
| other agreements. Before using the data, be sure to read the relevant |
| agreements carefully to ensure compliant use. Video copyrights belong to the |
| original video creators or platforms and are for academic research use only. |
| task_categories: |
| - visual-question-answering |
| - video-classification |
| extra_gated_fields: |
| Name: text |
| Company/Organization: text |
| Country: text |
| E-Mail: text |
| modalities: |
| - Video |
| - Text |
| configs: |
| - config_name: action_sequence |
| data_files: json/action_sequence.json |
| - config_name: moving_count |
| data_files: json/moving_count.json |
| - config_name: action_prediction |
| data_files: json/action_prediction.json |
| - config_name: episodic_reasoning |
| data_files: json/episodic_reasoning.json |
| - config_name: action_antonym |
| data_files: json/action_antonym.json |
| - config_name: action_count |
| data_files: json/action_count.json |
| - config_name: scene_transition |
| data_files: json/scene_transition.json |
| - config_name: object_shuffle |
| data_files: json/object_shuffle.json |
| - config_name: object_existence |
| data_files: json/object_existence.json |
| - config_name: fine_grained_pose |
| data_files: json/fine_grained_pose.json |
| - config_name: unexpected_action |
| data_files: json/unexpected_action.json |
| - config_name: moving_direction |
| data_files: json/moving_direction.json |
| - config_name: state_change |
| data_files: json/state_change.json |
| - config_name: object_interaction |
| data_files: json/object_interaction.json |
| - config_name: character_order |
| data_files: json/character_order.json |
| - config_name: action_localization |
| data_files: json/action_localization.json |
| - config_name: counterfactual_inference |
| data_files: json/counterfactual_inference.json |
| - config_name: fine_grained_action |
| data_files: json/fine_grained_action.json |
| - config_name: moving_attribute |
| data_files: json/moving_attribute.json |
| - config_name: egocentric_navigation |
| data_files: json/egocentric_navigation.json |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| --- |
| # MVBench |
|
|
| ## Dataset Description |
|
|
| - **Repository:** [MVBench](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb) |
| - **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005) |
| - **Point of Contact:** mailto:[kunchang li](likunchang@pjlab.org.cn) |
|
|
|
|
| ## <span style="color: red;">Important Update</span> |
| [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. |
|
|
|
|
|  |
|
|
| 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. |
|
|
|  |
|
|
| ## Evaluation |
|
|
| 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. |
|
|
| - **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. |
| - **Prompt**: We explore effective system prompts to encourage better temporal reasoning in MLLM, as well as efficient answer prompts for option extraction. |
|
|
| ## Leadrboard |
|
|
| While an [Online leaderboard]() is under construction, the current standings are as follows: |
|
|
|  |
