Update README.md

README.md

@@ -18,21 +18,28 @@ We're excited to unveil **Qwen2-VL**, the latest iteration of our Qwen-VL model,
 
 #### Key Enhancements:
 
-* **Enhanced Image Comprehension**: We've significantly improved the model's ability to understand and interpret visual information, setting new benchmarks across key performance metrics.
+* **SoTA understanding of images of various resolution & ratio**: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.
 
-* **Advanced Video Understanding**: Qwen2-VL now features superior online streaming capabilities, enabling real-time analysis of dynamic video content with remarkable accuracy.
+* **Understanding videos of 20min+**: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.
 
-* **Integrated Visual Agent Functionality**: Our model now seamlessly incorporates sophisticated system integration, transforming Qwen2-VL into a powerful visual agent capable of complex reasoning and decision-making.
+* **Agent that can operate your mobiles, robots, ...**: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions.
+
+* **Multilingual Support**: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.
 
-* **Expanded Multilingual Support**: We've broadened our language capabilities to better serve a diverse global user base, making Qwen2-VL more accessible and effective across different linguistic contexts.
 
 #### Model Architecture Updates:
 
 * **Naive Dynamic Resolution**: Unlike before, Qwen2-VL can handle arbitrary image resolutions, mapping them into a dynamic number of visual tokens, offering a more human-like visual processing experience.
 
+<p align="center">
+    <img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/qwen2_vl.jpg" width="80%"/>
+<p>
+
 * **Multimodal Rotary Position Embedding (M-ROPE)**: Decomposes positional embedding into parts to capture 1D textual, 2D visual, and 3D video positional information, enhancing its multimodal processing capabilities.
 
-![](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/qwen2_vl.jpg)
+<p align="center">
+    <img src="http://qianwen-res.oss-accelerate-overseas.aliyuncs.com/Qwen2-VL/mrope.png" width="80%"/>
+<p>
 
 We have three models with 2, 7 and 72 billion parameters. This repo contains the instruction-tuned 7B Qwen2-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2-vl/) and [GitHub](https://github.com/QwenLM/Qwen2-VL).