Update README.md

README.md

@@ -20,7 +20,7 @@ AVATAR is a **benchmark dataset** designed to evaluate **video-centric audio-vis
 Unlike previous benchmarks that rely on static image-level annotations and assume simplified conditions, AVATAR offers **high-resolution temporal annotations** over entire videos. It supports four challenging evaluation settings:  
 **Single-sound**, **Mixed-sound**, **Multi-entity**, and **Off-screen**.
 
-📄 [Paper (ICCV 2025)](https://hahyeon610.github.io/Video-centric_Audio_Visual_Localization/)  
+📄 [Paper (ICCV 2025)](https://arxiv.org/abs/2507.04667)  
 🌐 [Project Website](https://hahyeon610.github.io/Video-centric_Audio_Visual_Localization/)  
 📁 [Code & Data Viewer](https://huggingface.co/datasets/mipal/AVATAR/tree/main)
 
@@ -34,7 +34,7 @@ The dataset consists of the following files:
 |------|-------------|
 | `video.zip` | ~3.8GB of `.mp4` video clips |
 | `metadata.zip` | ~1.6GB of annotations (bounding boxes, segmentation masks, scenario tags) |
-| `vggsound_10k.txt` | List of 10,000 training video IDs from VGGSound |
+| `vggsound_10k.txt` | List of 10,000 training video IDs from [VGGSound](https://huggingface.co/datasets/Loie/VGGSound)|
 | `code/` | AVATAR benchmark evaluation code |
 
 Each annotated frame includes:
@@ -44,6 +44,25 @@ Each annotated frame includes:
 
 ---
 
+## 📊 Dataset Statistics
+
+AVATAR provides detailed quantitative statistics to help users understand its scale and diversity.
+
+| Type       | Count  |
+|------------|--------|
+| Videos     | 5,000  |
+| Frames     | 24,266 |
+| Off-screen | 670    |
+
+| Scenario Type   | Instances |
+|-----------------|-----------|
+| Total           | 28,516    |
+| Single-sound    | 15,372    |
+| Multi-entity    | 9,322     |
+| Mixed-sound     | 3,822     |
+
+---
+
 ## 🧪 Scenarios and Tasks
 
 AVATAR supports **fine-grained scenario-wise evaluation** of AVL models:
@@ -60,7 +79,20 @@ AVATAR supports **fine-grained scenario-wise evaluation** of AVL models:
 
 ---
 
-## 📋 Sample Instance (metadata)
+## 🧩 Audio-Visual Category Diversity
+
+AVATAR spans **80 audio-visual categories** covering a wide range of everyday domains, including:
+- **Human activities** (e.g., talking, singing)
+- **Music performances** (e.g., violin, drum, piano)
+- **Animal sounds** (e.g., dog barking, bird chirping)
+- **Vehicles** (e.g., car engine, helicopter)
+- **Tools and machines** (e.g., chainsaw, blender)
+
+Such diversity enables a **comprehensive evaluation** of model generalizability across varied audio-visual contexts.
+
+---
+
+## 📝 Example Metadata Format
 ```json
 {
   "video_id": str,
@@ -72,7 +104,7 @@ AVATAR supports **fine-grained scenario-wise evaluation** of AVL models:
             ...
           ],
       "bbox": [float, float, float, float], // (l, t, w, h),
-      "scenario": str, // "Single-Sound", "Mixed-Sound", "Multi-Entity", "Off-Screen"
+      "scenario": str, // "Single-sound", "Mixed-sound", "Multi-entity", "Off-screen"
       "audio_visual_category": str,
     },
     { // instance 2 (e.g., piano)