Update README.md

README.md

@@ -49,14 +49,26 @@ language:
 
 
 
-<!-- <h1 align="center">
-    JailbreakBench
-</h1>
--->
+
 <div align="center">
   <img src="figures/logo.png" alt="Image" />
 </div>
 
+<p align="center">
+    <p align="center">A Versatile Benchmark for Pavement Distress Perception and Interactive Vision-Language Analysis
+    <br>
+</p>
+
+<!-- <p align="center">
+    <p align="center"><b>NeurIPS 2024 Datasets and Benchmarks Track</b>
+    <br>
+</p> -->
+
+<!-- <h4 align="center">
+    <a href="https://arxiv.org/abs/2404.01318" target="_blank">Paper</a> |
+    <a href="https://jailbreakbench.github.io/"target="_blank">Leaderboard</a> |
+    <a href="https://github.com/JailbreakBench/jailbreakbench/" target="_blank">Benchmark code</a>
+</h4> -->
 ---
 
 
@@ -69,7 +81,7 @@ language:
 
 📍 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.
+📍 Stay tuned for updates — the Hugging Face repository will be updated with download links and detailed documentation once the paper is accepted.
 
 
 ## Abstract
@@ -82,7 +94,7 @@ PaveBench is a large-scale benchmark for pavement distress perception and intera
 
 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:
+The visual subset, **Multi-Task Visual Perception**, contains **20,124** high-resolution pavement images of size **512 × 512**. It supports:
 - image classification
 - object detection
 - semantic segmentation
@@ -93,9 +105,17 @@ In addition, the multimodal subset, **PaveVQA**, contains **32,160** question-an
 - **2,010** error-correction pairs
 
 These question-answer pairs cover recognition, localization, quantitative estimation, severity assessment, and maintenance recommendation. 
-## Distress Categories
 
-PaveBench includes six visual categories:
+The overall dataset statistics are summarized in the figure below.
+
+<div align="center">
+    <img src="figures/dataset_stat.png" width="75%">
+</div>
+
+
+## Multi-Task Visual Perception
+
+For the classification task, PaveBench includes six visual categories:
 - Longitudinal Crack
 - Transverse Crack
 - Alligator Crack
@@ -103,14 +123,13 @@ PaveBench includes six visual categories:
 - Pothole
 - Negative Sample
 
-These annotations are organized through a hierarchical pipeline covering classification, detection, and segmentation. 
+All images are annotated through a hierarchical multi-task pipeline, where image-level labels, instance-level bounding boxes, and pixel-level masks are constructed to support consistent evaluation across different perception settings.
 
-## Hard Distractors
-
-A key feature of PaveBench is its curated **hard-distractor subset**. During annotation, the dataset explicitly retains visually confusing real-world patterns such as:
+A key feature of PaveBench is its curated **hard-distractors**. During annotation, the dataset explicitly retains visually confusing real-world patterns such as:
 - pavement stains
-- shadows
+- tree 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. 
 
@@ -128,10 +147,12 @@ The questions are designed around practical pavement inspection needs, including
 - quantitative analysis
 - 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.
 
-## Dataset Statistics
+
+<!-- ## Dataset Statistics
 
 According to the paper:
 - Visual subset: **20,124** images
@@ -140,7 +161,7 @@ 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. 
+PaveBench is designed to provide a unified foundation for both precise visual perception and interactive multimodal reasoning in the pavement domain.  -->
 
 ## Benchmark Tasks