--- 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: - 1KImportant Update [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. ![images](./assert/generation.png) 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. ![images](./assert/task_example.png) ## 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: ![images](./assert/leaderboard.png)