Upload README.md

README.md

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-license: cc-by-nc-sa-4.0
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+# WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs
+
+
+<font size=7><div align='center' > [[🏠 Project Page](https://jaaackhongggg.github.io/WorldSense/)] [[📖 arXiv Paper](https://arxiv.org/pdf/2502.04326)] [[🤗 Dataset](https://huggingface.co/datasets/honglyhly/WorldSense)][[🏆 Leaderboard](https://jaaackhongggg.github.io/WorldSense/#leaderboard)]  </div></font>
+
+WorldSense is the **first** benchmark to assess the real-world omni-modal understanding with _visual, audio, and text_ input. 🌟
+
+
+---
+
+## 🔥 News
+* **`2024.06.03`** 🌟 We release Video-MME, the first benchmark for real-world omnimodal understanding of MLLMs.
+
+
+
+## 👀 WorldSense Overview
+
+we introduce **WorldSense**, the **first** benchmark to assess the multi-modal video understanding, that simultaneously encompasses _visual, audio, and text_ inputs. In contrast to existing benchmarks, our **WorldSense** has several features: 
+
+* **Collaboration of omni-modality**. We design the evaluation tasks to feature a strong coupling of audio and video, requiring models to effectively utilize the **synergistic perception of omni-modality**;
+* **Diversity of videos and tasks**. WorldSense encompasses a diverse collection of **1,662** audio-visual synchronised videos, systematically categorized into **8** primary domains and **67** fine-grained subcategories to cover the broad scenarios, and **3,172** multi-choice QA pairs across **26** distinct tasks to enable the comprehensive evaluation; 
+* **High-quality annotations**. All the QA pairs are manually labeled by 80 expert annotators with multiple rounds of correction to ensure quality. 
+
+Based on our **WorldSense**, we extensively evaluate various state-of-the-art models. The experimental results indicate that existing models face significant challenges in understanding real-world scenarios (48% best accuracy). We hope our **WorldSense** can provide a platform for evaluating the ability in constructing and understanding coherent contexts from omni-modality.
+
+
+
+<p align="center">
+    <img src="./asset/distribution.png" width="100%" height="100%">
+</p>
+
+## 📐 Dataset Examples
+
+<p align="center">
+    <img src="./asset/sample.png" width="100%" height="100%">
+</p>
+
+
+
+
+## 🔍 Dataset
+Please download our WorldSense from [here](https://huggingface.co/datasets/honglyhly/WorldSense).
+
+
+
+## 🔮 Evaluation Pipeline
+📍 **Evaluation**: 
+Thanks for [VLMEvalkit](https://github.com/open-compass/VLMEvalKit), we can perform the evaluation of current MLLMs on WorldSense easily. Please refer to [VLMEvalkit](https://github.com/open-compass/VLMEvalKit) for details.
+
+
+📍 **Leaderboard**: 
+
+If you want to add your model to our [leaderboard](https://jaaackhongggg.github.io/WorldSense/#leaderboard), please contact **jaaackhong@gmail.com**.
+
+
+## 📈 Experimental Results
+- **Evaluation results of sota MLLMs.**
+
+<p align="center">
+    <img src="./asset/overall_performance.png" width="96%" height="50%">
+</p>
+
+
+- **Fine-grained results on task category.**
+
+<p align="center">
+    <img src="./asset/fine_task.png" width="96%" height="50%">
+</p>
+
+- **Fine-grained results on audio type.**
+
+<p align="center">
+    <img src="./asset/fine_audio.png" width="96%" height="50%">
+</p>
+
+- **In-depth analysis for real-world omnimodal understanding.**
+
+<center>Impact of vision information.</center>
+<p align="center">
+    <img src="./asset/ablation_vision.png" width="96%" height="96%">
+</p>
+
+<center>Impact of audio information.</center>
+<p align="center">
+    <img src="./asset/ablation_audio.png" width="96%" height="96%">
+</p>
+
+<center>Impact of audio information for Video MLLMs.</center>
+<p align="center">
+    <img src="./asset/ablation_audio_v.png" width="96%" height="96%">
+</p>
+
+<center>Impact of video frames.</center>
+<p align="center">
+    <img src="./asset/video_frame_curve.png" width="96%" height="96%">
+</p>
+
+
+
+## 📖 Citation
+
+If you find WorldSense helpful for your research, please consider citing our work. Thanks!
+
+```bibtex
+@article{hong2025worldsenseevaluatingrealworldomnimodal,
+    title={WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs},
+    author={Jack Hong and Shilin Yan and Jiayin Cai and Xiaolong Jiang and Yao Hu and Weidi Xie},
+    year={2025},
+    eprint={2502.04326},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV},
+    url={https://arxiv.org/abs/2502.04326}, 
+}
+```