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README.md

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+# MVBench
 
 ## Dataset Description
 
 - **Repository:** [MVBench](https://github.com/OpenGVLab/Ask-Anything/blob/main/video_chat2/mvbench.ipynb)
-- **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005f)
+- **Paper:** [2311.17005](https://arxiv.org/abs/2311.17005)
 - **Point of Contact:** mailto:[kunchang li]([email protected])
 
+![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)
+
+## :telescope: 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.
+
+## :bar_chart: Leadrboard
+
+While an [Online leaderboard]() is under construction, the current standings are as follows:
+
+![images](./assert/leaderboard.png)