Add link to paper and GitHub repository

README.md

@@ -1,29 +1,29 @@
 ---
-license: cc-by-nc-4.0
-pretty_name: Urban-ImageNet
-task_categories:
-  - image-classification
-  - image-to-text
-  - text-to-image
-  - zero-shot-image-classification
-  - image-segmentation
 language:
-  - zh
-  - en
+- zh
+- en
+license: cc-by-nc-4.0
 size_categories:
-  - 1M<n<10M
+- 1M<n<10M
+task_categories:
+- image-classification
+- image-to-text
+- image-segmentation
+pretty_name: Urban-ImageNet
 tags:
-  - urban-perception
-  - social-media
-  - weibo
-  - image-text-retrieval
-  - instance-segmentation
-  - computational-urban-studies
-  - chinese-cities
+- urban-perception
+- social-media
+- weibo
+- image-text-retrieval
+- instance-segmentation
+- computational-urban-studies
+- chinese-cities
 ---
 
 # Urban-ImageNet
 
+[Paper](https://huggingface.co/papers/2605.09936) | [Code](https://github.com/yiasun/dataset-2)
+
 Urban-ImageNet is a large-scale multimodal dataset and benchmark for urban commercial space perception. It contains more than 2 million public Weibo image-text pairs collected from 61 commercial sites in 24 Chinese cities across 2019-2025. The dataset is organized by the HUSIC taxonomy, a 10-class framework for urban commercial imagery, and supports three benchmark tasks:
 
 - **T1 Urban scene semantic classification**
@@ -190,9 +190,9 @@ Prohibited uses include re-identification, account reconstruction, face recognit
 
 ```bibtex
 @misc{urbanimagenet2026,
-  title  = {Urban-ImageNet: A Large-Scale Multi-Modal Dataset for Urban Space Perception Benchmarking},
+  title  = {Urban-ImageNet: A Large-Scale Multi-Modal Dataset and Evaluation Framework for Urban Space Perception},
   author = {Urban-ImageNet Research Team},
   year   = {2026},
   note   = {Dataset and benchmark for NeurIPS 2026 Evaluations and Datasets Track}
 }
-```
+```