Update README.md

README.md

@@ -2,7 +2,7 @@
 pretty_name: PaveBench
 size_categories:
 - 10K<n<100K
-license: mit
+license: cc-by-nc-sa-4.0
 tags:
 - computer-vision
 - vision-language
@@ -12,19 +12,30 @@ tags:
 - image-classification
 - multimodal-learning
 - benchmark
+task_categories:
+- question-answering
+language:
+- en
 ---
 
 # PaveBench: A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis
 
-![PaveBench Overview](fig1.png)
+![PaveBench Overview](figures/PaveBench.png)
+
+📍 Data Availability: The dataset will be publicly released under the CC BY-NC-SA 4.0 license upon official acceptance of the associated paper.
+
+Stay tuned for updates — the Hugging Face repository will be updated with download links and detailed documentation once the paper is accepted.
+
 
 ## Abstract
 
-PaveBench is a large-scale benchmark for pavement distress perception and interactive vision-language analysis on real-world highway inspection images. It supports four core tasks: classification, object detection, semantic segmentation, and vision-language question answering. On the visual side, PaveBench provides large-scale annotations on real top-down pavement images and includes a curated hard-distractor subset for robustness evaluation. On the multimodal side, it introduces PaveVQA, a real-image question answering dataset supporting single-turn, multi-turn, and expert-corrected interactions, covering recognition, localization, quantitative estimation, and maintenance reasoning. :contentReference[oaicite:2]{index=2}
+PaveBench is a large-scale benchmark for pavement distress perception and interactive vision-language analysis on real-world highway inspection images. It supports four core tasks: classification, object detection, semantic segmentation, and vision-language question answering. On the visual side, PaveBench provides large-scale annotations on real top-down pavement images and includes a curated hard-distractor subset for robustness evaluation. On the multimodal side, it introduces PaveVQA, a real-image question answering dataset supporting single-turn, multi-turn, and expert-corrected interactions, covering recognition, localization, quantitative estimation, and maintenance reasoning.
+
+
 
 ## About the Dataset
 
-PaveBench is built on real-world highway inspection images collected in Liaoning Province, China, using a highway inspection vehicle equipped with a high-resolution line-scan camera. The captured images are top-down orthographic pavement views, which preserve the geometric properties of distress patterns and support reliable downstream quantification. The dataset provides unified annotations for multiple pavement distress tasks and is designed to connect visual perception with interactive vision-language analysis. :contentReference[oaicite:3]{index=3}
+PaveBench is built on real-world highway inspection images collected in Liaoning Province, China, using a highway inspection vehicle equipped with a high-resolution line-scan camera. The captured images are top-down orthographic pavement views, which preserve the geometric properties of distress patterns and support reliable downstream quantification. The dataset provides unified annotations for multiple pavement distress tasks and is designed to connect visual perception with interactive vision-language analysis.
 
 The visual subset contains **20,124** high-resolution pavement images of size **512 × 512**. It supports:
 - image classification
@@ -36,8 +47,7 @@ In addition, the multimodal subset, **PaveVQA**, contains **32,160** question-an
 - **20,100** multi-turn interactions
 - **2,010** error-correction pairs
 
-These question-answer pairs cover recognition, localization, quantitative estimation, severity assessment, and maintenance recommendation. :contentReference[oaicite:4]{index=4}
-
+These question-answer pairs cover recognition, localization, quantitative estimation, severity assessment, and maintenance recommendation. 
 ## Distress Categories
 
 PaveBench includes six visual categories:
@@ -48,7 +58,7 @@ PaveBench includes six visual categories:
 - Pothole
 - Negative Sample
 
-These annotations are organized through a hierarchical pipeline covering classification, detection, and segmentation. :contentReference[oaicite:5]{index=5}
+These annotations are organized through a hierarchical pipeline covering classification, detection, and segmentation. 
 
 ## Hard Distractors
 
@@ -57,7 +67,7 @@ A key feature of PaveBench is its curated **hard-distractor subset**. During ann
 - shadows
 - road markings
 
-These distractors often co-occur with real pavement distress and closely resemble true distress patterns, making the benchmark more realistic and more challenging for robustness evaluation. :contentReference[oaicite:6]{index=6} :contentReference[oaicite:7]{index=7}
+These distractors often co-occur with real pavement distress and closely resemble true distress patterns, making the benchmark more realistic and more challenging for robustness evaluation. 
 
 ## PaveVQA
 
@@ -74,7 +84,7 @@ The questions are designed around practical pavement inspection needs, including
 - severity assessment
 - maintenance recommendation
 
-Structured metadata derived from visual annotations, such as bounding boxes, pixel area, and skeleton length, is used to support grounded and low-hallucination question answering. :contentReference[oaicite:8]{index=8}
+Structured metadata derived from visual annotations, such as bounding boxes, pixel area, and skeleton length, is used to support grounded and low-hallucination question answering.
 
 ## Dataset Statistics
 
@@ -85,7 +95,7 @@ According to the paper:
 - Four primary analysis tasks
 - Fourteen fine-grained VQA sub-categories
 
-PaveBench is designed to provide a unified foundation for both precise visual perception and interactive multimodal reasoning in the pavement domain. :contentReference[oaicite:10]{index=10}
+PaveBench is designed to provide a unified foundation for both precise visual perception and interactive multimodal reasoning in the pavement domain. 
 
 ## Benchmark Tasks
 
@@ -95,7 +105,7 @@ PaveBench supports four core tasks:
 3. Semantic Segmentation
 4. Vision-Language Question Answering
 
-It also includes an agent-augmented evaluation setting where vision-language models are combined with domain-specific tools for more reliable quantitative analysis. :contentReference[oaicite:11]{index=11} :contentReference[oaicite:12]{index=12}
+It also includes an agent-augmented evaluation setting where vision-language models are combined with domain-specific tools for more reliable quantitative analysis. 
 
 ## Usage
 
@@ -104,4 +114,9 @@ Example usage with `datasets`:
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("MML-Group/PaveBench")
+dataset = load_dataset("MML-Group/PaveBench")
+```
+
+---
+license: cc-by-nc-sa-4.0
+---