Update task categories and add paper, code, and project links

README.md

@@ -1,12 +1,14 @@
 ---
+language:
+- en
 license: cc-by-4.0
+size_categories:
+- 10K<n<100K
 task_categories:
-- visual-question-answering
+- image-text-to-text
 - image-segmentation
 - depth-estimation
-- image-to-text
-language:
-- en
+pretty_name: 'PAVE: Pedestrian Accessibility Vision–Language Dataset'
 tags:
 - vision-language
 - multimodal
@@ -22,9 +24,6 @@ tags:
 - real-world
 - grounded-conversation
 - urban-scenes
-pretty_name: 'PAVE: Pedestrian Accessibility Vision–Language Dataset'
-size_categories:
-- 10K<n<100K
 ---
 
 # PAVE: Pedestrian Accessibility and Visual-grounded Evaluation
@@ -33,7 +32,7 @@ size_categories:
 
 > **WalkGPT: Grounded Vision–Language Conversation with Depth-Aware Segmentation for Pedestrian Navigation**  
 > *(Accepted at CVPR 2026)*  
-> Paper link: *(Coming soon)*
+> [Paper](https://huggingface.co/papers/2603.10703) | [Code](https://github.com/rafiibnsultan/WalkGPT) | [Project Page](https://sites.google.com/view/walkgpt-26/home)
 
 PAVE is a spatially grounded VQA benchmark for accessibility-aware reasoning in real-world pedestrian environments, 
 unifying language understanding, pixel-level grounding, and depth-aware navigation guidance.
@@ -175,4 +174,5 @@ If you use PAVE, please cite:
   author={Rafi Ibn Sultan, Hui Zhu, Xiangyu Zhou, Chengyin Li, Prashant Khanduri, Marco Brocanelli, Dongxiao Zhu},
   booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   year={2026}
-}
+}
+```