Update README.md

README.md

@@ -10,7 +10,7 @@ tags:
 - benchmark
 language:
 - en
-pretty_name: Tracking-Any-Granularity
+pretty_name: TAG
 arxiv: 2510.18822
 configs:
 - config_name: default
@@ -30,18 +30,50 @@ huggingface-cli download MCG-NJU/Tracking-Any-Granularity --repo-type dataset --
 
 ## Dataset Summary
 
-Tracking-Any-Granularity is a comprehensive dataset for training our unified model, termed Tracking-Any-Granularity (TAG), with annotations across three granularities: segmentation masks, bounding boxes, and key points.
+**T**racking-**A**ny-**G**ranularity (TAG) is a comprehensive dataset for training our unified model, termed Tracking-Any-Granularity (TAG), with annotations across three granularities: segmentation masks, bounding boxes, and key points.
+
+  <table  align="center">
+    <tbody>
+      <tr>
+        <td><img  width="220" src="assets/data/00025.gif"/></td>
+        <td><img  width="220" src="assets/data/00076.gif"/></td>
+        <td><img  width="220" src="assets/data/00045.gif"/></td>
+      </tr>
+    </tbody>
+  </table>
+
+  <table  align="center">
+    <tbody>
+      <tr>
+        <td><img  width="220" src="assets/data/00102.gif"/></td>
+        <td><img  width="220" src="assets/data/00103.gif"/></td>
+        <td><img  width="220" src="assets/data/00152.gif"/></td>
+      </tr>
+    </tbody>
+  </table>
+
+  <table  align="center">
+    <tbody>
+      <tr>
+        <td><img  width="220" src="assets/data/00227.gif"/></td>
+        <td><img  width="220" src="assets/data/00117.gif"/></td>
+        <td><img  width="220" src="assets/data/00312.gif"/></td>
+      </tr>
+    </tbody>
+  </table>
+
 
 ## Dataset Description
 
-Our dataset includes a wide range of video sources, demonstrating strong diversity and serving as a solid benchmark for evaluating tracking performance. Each video sequence is annotated with 18 attributes representing different tracking challenges, which can appear simultaneously in the same video. Common challenges include motion blur, deformation, and partial occlusion, reflecting the dataset’s high difficulty. Most videos contain multiple attributes, indicating the dataset’s coverage of complex and diverse tracking scenarios.
+Our dataset includes **a wide range of video sources**, demonstrating strong diversity and serving as a solid benchmark for evaluating tracking performance. Each video sequence is annotated with **18 attributes representing different tracking challenges**, which can appear simultaneously in the same video. Common challenges include motion blur, deformation, and partial occlusion, reflecting the dataset’s high difficulty. Most videos contain multiple attributes, indicating the dataset’s coverage of complex and diverse tracking scenarios.
+
+![TAG dataset](assets/4-attr.png?raw=true)
 
 ## Benchmark Results
 
 We evaluated many representative trackers on the valid and test splits of our dataset:
 
-- video object segmentation
-
+*video object segmentation*
 | Model                         | 𝒥 & ℱ | 𝒥 | ℱ | 𝒥 & ℱ | 𝒥 | ℱ |
 |-------------------------------|---------|---------|---------|---------|---------|---------|
 | STCN                           | 70.4    | 65.9    | 75      | 76.2    | 72.2    | 80.2    |
@@ -56,8 +88,7 @@ We evaluated many representative trackers on the valid and test splits of our da
 | JointFormer                    | 76.6    | 72.8    | 80.5    | 79.1    | 75.5    | 82.7    |
 | SAM2++                           | 87.4    | 84.2    | 90.7    | 87.9    | 84.9    | 90.9    |
 
-- single object tracking
-
+*single object tracking*
 | Model                        | AUC | P_Norm | P | AUC | P_Norm | P |
 |------------------------------|---------|---------|---------|---------|---------|---------|
 | OSTrack                       | 74.8    | 84.4    | 72.7    | 69.7    | 78.8    | 69.9    |
@@ -73,8 +104,7 @@ We evaluated many representative trackers on the valid and test splits of our da
 | LoRAT                         | 75.1    | 84.8    | 74.4    | 70.5    | 79.7    | 68.7    |
 | SAM2++                          | 80.7    | 89.7    | 77.8    | 78      | 85.7    | 81.5    |
 
-- point tracking
-
+*point tracking*
 | Model      | Acc | Acc |
 |------------|---------|---------|
 | pips       | 19.0      | 19.8    |