Create README.md

README.md

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+---
+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: test
+  data_files: test.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)
+
+![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)