AV-Odyssey/AV_Odyssey_Bench

Official dataset for the paper "AV-Odyssey: Can Your Multimodal LLMs Really Understand Audio-Visual Information?".

🌟 For more details, please refer to the project page with data examples: https://av-odyssey.github.io/.

[🌐 Webpage] [📖 Paper] [🤗 Huggingface AV-Odyssey Dataset] [🤗 Huggingface Deaftest Dataset] [🏆 Leaderboard]

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🔥 News

• 2024.11.24 🌟 We release AV-Odyssey, the first-ever comprehensive evaluation benchmark to explore whether MLLMs really understand audio-visual information.

👀 About AV-Odyssey

Recently, multimodal large language models (MLLMs), such as GPT-4o, Gemini 1.5 Pro, and Reka Core, have expanded their capabilities to include vision and audio modalities. While these models demonstrate impressive performance across a wide range of audio-visual applications, our proposed DeafTest reveals that MLLMs often struggle with simple tasks humans find trivial: 1) determining which of two sounds is louder, and 2) determining which of two sounds has a higher pitch. Motivated by these observations, we introduce AV-Odyssey Bench. This benchmark encompasses 26 different tasks and 4,555 carefully crafted problems, each incorporating text, visual, and audio components. All data are newly collected and annotated by humans, not from any existing audio-visual dataset. AV-Odyssey Bench demonstrates three major features: 1. Comprehensive Audio Attributes; 2. Extensive Domains; 3. Interleaved Text, Audio, and Visual components.

📐 Data Examples

Please refer to our project page https://av-odyssey.github.io/ for exploring more examples.

📍AV-Odyssey Bench

🔍 Dataset

License:

AV-Odyssey is only used for academic research. Commercial use in any form is prohibited.
The copyright of all videos belongs to the video owners.
If there is any infringement in AV-Odyssey, please email [email protected] and we will remove it immediately.
Without prior approval, you cannot distribute, publish, copy, disseminate, or modify AV-Odyssey in whole or in part. 
You must strictly comply with the above restrictions.

Please send an email to [email protected]. 🌟

🏆 Leaderboard

Contributing to the AV-Odyssey Leaderboard

🚨 The Leaderboard for AV-Odyssey is continuously being updated, welcoming the contribution of your excellent MLLMs!

Citation

If you find our work helpful for your research, please consider citing our work.

@misc{gong2024avodysseybenchmultimodalllms,
      title={AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?}, 
      author={Kaixiong Gong and Kaituo Feng and Bohao Li and Yibing Wang and Mofan Cheng and Shijia Yang and Jiaming Han and Benyou Wang and Yutong Bai and Zhuoran Yang and Xiangyu Yue},
      year={2024},
      eprint={2412.02611},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.02611}, 
}