[update] README

README.md

@@ -1,3 +1,174 @@
 ---
 license: mit
+title: "Trokens: Semantic-Aware Relational Trajectory Tokens Dataset"
+tags:
+- computer-vision
+- action-recognition
+- few-shot-learning
+- video-understanding
+- point-tracking
+size_categories:
+- 100K<n<1M
+task_categories:
+- video-classification
 ---
+
+# Trokens Dataset: Semantic-Aware Relational Trajectory Tokens for Few-shot Action Recognition
+
+This dataset contains the preprocessed data for "Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition" (ICCV 2025).
+
+[**Paper**]() | [**Project Page**](https://www.cs.umd.edu/~pulkit/trokens/) | [**Code**](https://github.com/pulkitkumar95/trokens)
+
+## Dataset Overview
+
+This dataset provides semantic-aware relational trajectory tokens (Trokens) extracted from multiple action recognition datasets, designed specifically for few-shot action recognition tasks. The dataset includes semantically meaningful point trajectories extracted using CoTracker3 and DINOv2 features, along with few-shot episode split information.
+
+## Dataset Structure
+
+The dataset contains two main components:
+
+### 1. Point Tracking Data (`cotracker3_bip_fr_32/`)
+
+Semantic point trajectories extracted using CoTracker3 with bipartite clustering on DINOv2 features:
+
+```
+cotracker3_bip_fr_32/
+└── {dataset_name}/
+    └── feat_dump/
+        └── {video_name}.pkl
+```
+
+Each pickle file contains:
+- **`pred_tracks`**: Tracked point coordinates across frames [T, N, 2]
+- **`pred_visibility`**: Visibility mask for each point [T, N]  
+- **`obj_ids`**: Object/cluster IDs for each point [N]
+- **`point_queries`**: Original query point indices [N]
+
+### 2. Few-shot Split Information (`few_shot_info/`)
+
+Data splits for few-shot learning evaluation across multiple datasets.
+
+## Point Extraction Details
+
+### Semantic Point Tracking
+- **Method**: CoTracker3 with semantic clustering on DINOv2 features
+- **Clustering**: Bipartite clustering for semantic entity detection
+- **Parameters**: 
+  - Clustering method: `bipartite`
+  - Number of frames for clustering: 32
+  - Points filtered based on spatial proximity to remove redundancy
+
+### Video Processing
+- **Frame Rate**: 
+  - Most datasets: 10 fps
+  - Something Something V2 (SSV2): 12 fps (original video fps)
+- **Point Filtering**: Redundant points removed based on spatial proximity
+- **GPU Acceleration**: CUDA support for efficient processing
+
+### Key Features
+- Robust point tracking across video frames using CoTracker3
+- Semantic point extraction through clustering on DINOv2 features
+- Point filtering to remove redundant tracks
+- Support for different clustering strategies and parameters
+
+## Supported Datasets
+
+The point tracking data is available for multiple action recognition datasets:
+- **Something Something V2 (SSV2)**
+- **Kinetics**
+- **UCF-101**
+- **HMDB-51**
+- And others
+
+## Usage
+
+### Loading Point Tracking Data
+
+```python
+import pickle
+import numpy as np
+
+# Load point tracking data for a video
+with open('cotracker3_bip_fr_32/{dataset}/{video_name}.pkl', 'rb') as f:
+    data = pickle.load(f)
+    
+pred_tracks = data['pred_tracks']      # [T, N, 2] - point coordinates
+pred_visibility = data['pred_visibility']  # [T, N] - visibility mask
+obj_ids = data['obj_ids']              # [N] - cluster/object IDs
+point_queries = data['point_queries']  # [N] - query point indices
+```
+
+### Loading Few-shot Splits
+
+```python
+# Load few-shot episode information
+# (Structure depends on specific dataset format)
+```
+
+## Applications
+
+This dataset is designed for:
+- **Few-shot Action Recognition**: Training models with limited labeled examples
+- **Video Understanding**: Learning from semantic-aware relational trajectory tokens (Trokens)
+- **Point Tracking Research**: Semantic point trajectory analysis
+- **Action Recognition**: General video classification tasks
+
+## Technical Details
+
+### Dependencies
+- PyTorch
+- NumPy  
+- Pandas
+- Einops
+- CoTracker3 (for point tracking)
+- DINOv2 (for feature extraction)
+
+### Point Extraction Pipeline
+1. **Feature Extraction**: DINOv2 features computed for video frames
+2. **Semantic Clustering**: Bipartite clustering to identify semantic entities
+3. **Point Sampling**: Points sampled from cluster centers
+4. **Trajectory Tracking**: CoTracker3 used to track points across frames
+5. **Post-processing**: Redundant points filtered based on spatial proximity
+
+## Citation
+
+If you use this dataset in your research, please cite our papers:
+
+```bibtex
+@inproceedings{kumar2025trokens,
+  title={Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition},
+  author={Kumar, Pulkit and Huang, Shuaiyi and Walmer, Matthew and Rambhatla, Sai Saketh and Shrivastava, Abhinav},
+  booktitle={International Conference on Computer Vision},
+  year={2025}
+}
+
+@inproceedings{kumar2024trajectory,
+  title={Trajectory-aligned Space-time Tokens for Few-shot Action Recognition},
+  author={Kumar, Pulkit and Padmanabhan, Namitha and Luo, Luke and Rambhatla, Sai Saketh and Shrivastava, Abhinav},
+  booktitle={European Conference on Computer Vision},
+  pages={474--493},
+  year={2024},
+  organization={Springer}
+}
+```
+
+## Authors
+
+[**Pulkit Kumar***](https://www.cs.umd.edu/~pulkit/)<sup>1</sup> · [**Shuaiyi Huang***](https://shuaiyihuang.github.io/)<sup>1</sup> · [**Matthew Walmer**](https://www.cs.umd.edu/~mwalmer/)<sup>1</sup> · [**Sai Saketh Rambhatla**](https://rssaketh.github.io)<sup>1,2</sup> · [**Abhinav Shrivastava**](http://www.cs.umd.edu/~abhinav/)<sup>1</sup>
+
+<sup>1</sup>University of Maryland, College Park&emsp;&emsp;&emsp;&emsp;<sup>2</sup>GenAI, Meta<br>
+<sup>*Equal contribution</sup>
+
+## License
+
+This dataset is released under the MIT License.
+
+## Acknowledgments
+
+This dataset is built upon:
+- [CoTracker](https://github.com/facebookresearch/co-tracker): For robust point tracking
+- [TATs](https://github.com/pulkitkumar95/tats): Trajectory-aligned Space-time Tokens for Few-shot Action Recognition
+- [DINOv2](https://github.com/facebookresearch/dinov2): For semantic feature extraction
+
+
+We thank the authors for making their code publicly available.