Update README.md

README.md

@@ -544,6 +544,22 @@ Urban-ImageNet is derived from **public Weibo posts** — posts whose visibility
 
 ---
 
+## Related Work
+
+Urban-ImageNet is designed as a **domain-specific complement** to the following general-purpose benchmarks:
+
+| Benchmark                                                    | Task Covered                   | Relation to Urban-ImageNet                                   |
+| ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
+| [Places365](http://places2.csail.mit.edu/)                   | Scene classification           | Urban-ImageNet provides theory-grounded, activation-aware sub-categories of Places365 classes |
+| [SUN Database](https://3dvision.princeton.edu/projects/2010/SUN/) | Scene classification           | Complementary focus on commercial urban spaces with social context |
+| [MS-COCO Captions](https://cocodataset.org/)                 | Image–text retrieval           | Urban-ImageNet provides authentic first-person social media narratives vs. COCO's objective third-person captions |
+| [Flickr30K](http://shannon.cs.illinois.edu/DenotationGraph/) | Image–text retrieval           | Urban-ImageNet provides Chinese-language, domain-specific, multi-positive retrieval ground truth |
+| [MS-COCO Instance Seg.](https://cocodataset.org/)            | Instance segmentation          | Urban-ImageNet provides domain-specific commercial-space vocabulary (retail shelves, escalators, hotel beds, etc.) |
+| [Cityscapes](https://www.cityscapes-dataset.com/)            | Semantic/instance segmentation | Urban-ImageNet focuses on commercial interior and mixed exterior spaces vs. Cityscapes' driving-scene focus |
+
+---
+
+
 ## Citation
 
 If you use Urban-ImageNet in your research, please cite our paper:
@@ -567,21 +583,6 @@ If you use Urban-ImageNet in your research, please cite our paper:
 
 ---
 
-## Related Work
-
-Urban-ImageNet is designed as a **domain-specific complement** to the following general-purpose benchmarks:
-
-| Benchmark                                                    | Task Covered                   | Relation to Urban-ImageNet                                   |
-| ------------------------------------------------------------ | ------------------------------ | ------------------------------------------------------------ |
-| [Places365](http://places2.csail.mit.edu/)                   | Scene classification           | Urban-ImageNet provides theory-grounded, activation-aware sub-categories of Places365 classes |
-| [SUN Database](https://vision.cs.princeton.edu/projects/2010/SUN/) | Scene classification           | Complementary focus on commercial urban spaces with social context |
-| [MS-COCO Captions](https://cocodataset.org/)                 | Image–text retrieval           | Urban-ImageNet provides authentic first-person social media narratives vs. COCO's objective third-person captions |
-| [Flickr30K](http://shannon.cs.illinois.edu/DenotationGraph/) | Image–text retrieval           | Urban-ImageNet provides Chinese-language, domain-specific, multi-positive retrieval ground truth |
-| [MS-COCO Instance Seg.](https://cocodataset.org/)            | Instance segmentation          | Urban-ImageNet provides domain-specific commercial-space vocabulary (retail shelves, escalators, hotel beds, etc.) |
-| [Cityscapes](https://www.cityscapes-dataset.com/)            | Semantic/instance segmentation | Urban-ImageNet focuses on commercial interior and mixed exterior spaces vs. Cityscapes' driving-scene focus |
-
----
-
 ## License
 
 The dataset is released under **Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)**.