Update README.md

README.md

@@ -78,11 +78,11 @@ dataset_info:
 
 
 <p align="center">
-   <img src="images/TDBench.jpg" width="500"></a>
+   <img src="images/TDBench.jpg" width="600"></a>
 </p>
-<p align="justify"> <b> Figure</b>: 8 Representative VLMs on 10 dimensions in TDBench
+<p align="center"> 8 Representative VLMs on 10 dimensions in TDBench </p>
 
-**<p align="justify"> Abstract:** The rapid emergence of Vision-Language Models (VLMs) has significantly advanced multimodal understanding, enabling applications in scene comprehension and visual reasoning. While these models have been primarily evaluated and developed for front-view image understanding, their capabilities in interpreting top-down images have received limited attention, partly due to the scarcity of diverse top-down datasets and the challenges in collecting such data. In contrast, top-down vision provides explicit spatial overviews and improved contextual understanding of scenes, making it particularly valuable for tasks like autonomous navigation, aerial imaging, and spatial planning. In this work, we address this gap by introducing TDBench, a comprehensive benchmark for VLMs in top-down image understanding. TDBench is constructed from public top-down view datasets and high-quality simulated images, including diverse real-world and synthetic scenarios. TDBench consists of visual question-answer pairs across ten evaluation dimensions of image understanding. Moreover, we conduct four case studies that commonly happen in real-world scenarios but are less explored. By revealing the strengths and limitations of existing VLM through evaluation results, we hope TDBench to provide insights for motivating future research. </p>
+**Abstract:** The rapid emergence of Vision-Language Models (VLMs) has significantly advanced multimodal understanding, enabling applications in scene comprehension and visual reasoning. While these models have been primarily evaluated and developed for front-view image understanding, their capabilities in interpreting top-down images have received limited attention, partly due to the scarcity of diverse top-down datasets and the challenges in collecting such data. In contrast, top-down vision provides explicit spatial overviews and improved contextual understanding of scenes, making it particularly valuable for tasks like autonomous navigation, aerial imaging, and spatial planning. In this work, we address this gap by introducing TDBench, a comprehensive benchmark for VLMs in top-down image understanding. TDBench is constructed from public top-down view datasets and high-quality simulated images, including diverse real-world and synthetic scenarios. TDBench consists of visual question-answer pairs across ten evaluation dimensions of image understanding. Moreover, we conduct four case studies that commonly happen in real-world scenarios but are less explored. By revealing the strengths and limitations of existing VLM through evaluation results, we hope TDBench to provide insights for motivating future research.
 
 
 ## 📢 Latest Updates
@@ -110,7 +110,7 @@ dataset_info:
 </p>
 
 
-> <p align="justify"> <b> <span style="color: blue;">Table</span></b>: Overview performance of 8 open source VLMs and 6 propriety VLMs on 10 dimensions with RotationalEval method. </p>
+<p align="center"> Overview performance of 8 open source VLMs and 6 propriety VLMs on 10 dimensions with RotationalEval method. </p>
 
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@@ -222,11 +222,9 @@ If you find our work and this repository useful, please consider giving our repo
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 ## 📨 Contact
 If you have any questions, please create an issue on this repository or contact at kh3119@columbia.edu or
 mz2866@columbia.edu.
 
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-[<img src="images/ICSL_Logo.png" height="80">](http://icsl.ee.columbia.edu/)
+[<img src="images/ICSL_Logo.png" width="500"/>](http://icsl.ee.columbia.edu/)