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README.md

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-# WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs
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+<br>
+<h1>WorldSense: Evaluating Real-world Omnimodal Understanding for Multimodal LLMs</h1>
 
+Jack Hong<sup>1</sup>, [Shilin Yan](https://scholar.google.com/citations?user=2VhjOykAAAAJ&hl=zh-CN&oi=ao)<sup>1†</sup>, Jiayin Cai<sup>1</sup>, [Xiaolong Jiang](https://scholar.google.com/citations?user=G0Ow8j8AAAAJ&hl=zh-CN&oi=ao)<sup>1</sup>, [Yao Hu](https://scholar.google.com/citations?user=LIu7k7wAAAAJ&hl=en)<sup>1</sup>, [Weidi Xie](https://scholar.google.com/citations?user=Vtrqj4gAAAAJ&hl=en)<sup>2‡</sup>
 
-<font size=3><div align='left' > [[🏠 Project Page](https://jaaackhongggg.github.io/WorldSense/)] [[📖 arXiv Paper](https://arxiv.org/pdf/2502.04326)] [[🔍 Eval Code](https://github.com/open-compass/VLMEvalKit/tree/main)] [[🤗 Dataset](https://huggingface.co/datasets/honglyhly/WorldSense)] [[🏆 Leaderboard](https://jaaackhongggg.github.io/WorldSense/#leaderboard)]  </div></font>
+<div class="is-size-6 publication-authors">
+  <p class="footnote">
+    <span class="footnote-symbol"><sup>†</sup></span>Project Leader
+    <span class="footnote-symbol"><sup>‡</sup></span>Corresponding Author
+  </p>
+</div>
 
-WorldSense is the **first** benchmark to assess the real-world omni-modal understanding with _visual, audio, and text_ input. 🌟
+<sup>1</sup>Xiaohongshu Inc. <sup>2</sup>Shanghai Jiao Tong University
 
+<font size=3><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>
+</div>
 
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 ## 🔥 News
-* **`2024.06.03`** 🌟 We release Video-MME, the first benchmark for real-world omnimodal understanding of MLLMs.
+* **`2024.06.03`** 🌟 We release WorldSense, the first benchmark for real-world omnimodal understanding of MLLMs.
 
 
 
@@ -45,7 +55,7 @@ Please download our WorldSense from [here](https://huggingface.co/datasets/hongl
 
 ## 🔮 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.
+Thanks for the reproduction of our evaluation through [VLMEvalkit](https://github.com/open-compass/VLMEvalKit). Please refer to [VLMEvalkit](https://github.com/open-compass/VLMEvalKit) for details.
 
 
 📍 **Leaderboard**: