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Datasets:
OpenGVLab
/
MVBench

Tasks:
Visual Question Answering
Video Classification
Modalities:
Image
Tabular
Text
Formats:
json
Languages:
English
Size:
1K - 10K
ArXiv:
Libraries:
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pandas
License:
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metadata
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

images

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

Evaluation

An evaluation example is provided in 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