Datasets:

pulkitkumar95
/

trokens_pt_data

@@ -1,174 +1,173 @@
----
-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.

+---
+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.