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+---
+license: mit
+task_categories:
+- video-text-retrieval
+- text-to-video
+language:
+- en
+tags:
+- video-retrieval
+- generative-retrieval
+- semantic-ids
+- text-to-video
+size_categories:
+- 10K<n<100K
+---
+
+# GRDR-TVR: Generative Recall, Dense Reranking for Text-to-Video Retrieval
+
+This dataset contains the pre-extracted video features and trained model checkpoints for the GRDR (Generative Recall, Dense Reranking) framework for efficient Text-to-Video Retrieval (TVR).
+
+## 📄 Paper
+
+**Generative Recall, Dense Reranking: Learning Multi-View Semantic IDs for Efficient Text-to-Video Retrieval**
+
+*Conference: SIGIR 2026*
+
+[Paper PDF](https://arxiv.org/abs/XXXX.XXXXX) | [Code Repository](https://github.com/JasonCoderMaker/GRDR)
+
+## 📊 Dataset Overview
+
+This dataset includes three main components:
+
+### 1. InternVideo2 Features (~3.4GB)
+Pre-extracted video features using InternVideo2 encoder for four benchmark datasets:
+- **MSR-VTT**: 10,000 videos (932MB)
+- **ActivityNet**: 20,000 videos (1.1GB)
+- **DiDeMo**: 10,464 videos (916MB)
+- **LSMDC**: 1,000 movies, 118,081 clips (424MB)
+
+**Feature Details:**
+- Dimension: 512-d embeddings
+- Format: Pickle files (`.pkl`) with `{video_id: embedding}` mappings
+- Extraction: InternVideo2 (InternVL-2B) with temporal pooling
+
+### 2. GRDR Model Checkpoints (~2GB)
+Trained GRDR models (T5-small based) for all four datasets:
+- **MSR-VTT**: 494MB
+- **ActivityNet**: 498MB  
+- **DiDeMo**: 504MB
+- **LSMDC**: 478MB
+
+**Checkpoint Components:**
+- `best_model.pt` - Complete model checkpoint
+- `best_model.pt.model` - T5 encoder-decoder weights
+- `best_model.pt.videorqvae` - Video RQ-VAE quantizer
+- `best_model.pt.code` - Pre-computed semantic IDs
+- `best_model.pt.centroids` - Codebook centroids
+- `best_model.pt.embedding` - Learned embeddings
+- `best_model.pt.start_token` - Start token embeddings
+
+**Model Architecture:**
+- Base: T5-small (60M parameters)
+- Codebook size: 128/96/200 (dataset-dependent)
+- Max code length: 3
+- Training: 3-phase progressive training
+
+### 3. Xpool Reranker Checkpoints (~7.2GB)
+Pre-trained reranker models for dense reranking stage:
+- **MSR-VTT**: msrvtt9k_model_best.pth (1.8GB)
+- **ActivityNet**: actnet_model_best.pth (1.8GB)
+- **DiDeMo**: didemo_model_best.pth (1.8GB)
+- **LSMDC**: lsmdc_model_best.pth (1.8GB)
+
+**Reranker Details:**
+- Architecture: CLIP-based (ViT-B/32)
+- Purpose: Fine-grained reranking of recalled candidates
+- Format: PyTorch checkpoint files (`.pth`)
+
+## 🚀 Quick Start
+
+### Download Specific Components
+
+#### Using Python Script
+
+```bash
+# Download everything
+python download_features.py --all
+
+# Download only InternVideo2 features for specific datasets
+python download_features.py --features --datasets msrvtt actnet
+
+# Download GRDR checkpoints only
+python download_features.py --grdr
+
+# Download Xpool reranker only
+python download_features.py --xpool --datasets msrvtt
+```
+
+#### Using Hugging Face CLI
+
+```bash
+# Download entire dataset
+huggingface-cli download JasonCoderMaker/GRDR-TVR --repo-type dataset --local-dir ./GRDR-TVR
+
+# Download specific component
+huggingface-cli download JasonCoderMaker/GRDR-TVR InternVideo2/msrvtt --repo-type dataset --local-dir ./features
+
+# Download GRDR checkpoint for MSR-VTT
+huggingface-cli download JasonCoderMaker/GRDR-TVR GRDR/msrvtt --repo-type dataset --local-dir ./checkpoints
+```
+
+### Load Features in Python
+
+```python
+import pickle
+from huggingface_hub import hf_hub_download
+
+# Download and load InternVideo2 features
+feature_file = hf_hub_download(
+    repo_id="JasonCoderMaker/GRDR-TVR",
+    filename="InternVideo2/msrvtt/msrvtt_internvideo2.pkl",
+    repo_type="dataset"
+)
+
+with open(feature_file, 'rb') as f:
+    video_features = pickle.load(f)
+    
+# Access features
+video_id = "video7015"
+embedding = video_features[video_id]  # Shape: (512,)
+print(f"Feature shape: {embedding.shape}")
+```
+
+### Load GRDR Model
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Download checkpoint
+checkpoint_path = hf_hub_download(
+    repo_id="JasonCoderMaker/GRDR-TVR",
+    filename="GRDR/msrvtt/best_model/best_model.pt",
+    repo_type="dataset"
+)
+
+# Load model
+checkpoint = torch.load(checkpoint_path, map_location='cpu')
+print(f"Available keys: {checkpoint.keys()}")
+
+# Use with your GRDR model
+from models.grdr import GRDR
+model = GRDR(...)
+model.load_state_dict(checkpoint['model'], strict=False)
+```
+
+### Load Xpool Reranker
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+
+# Download reranker checkpoint
+reranker_path = hf_hub_download(
+    repo_id="JasonCoderMaker/GRDR-TVR",
+    filename="Xpool/msrvtt9k_model_best.pth",
+    repo_type="dataset"
+)
+
+# Load reranker
+checkpoint = torch.load(reranker_path, map_location='cpu')
+# Use with your Xpool model
+```
+
+## 📂 Repository Structure
+
+```
+GRDR-TVR/
+├── README.md                          # This file
+├── download_features.py               # Python download utility
+├── download_checkpoints.sh            # Bash download script
+│
+├── InternVideo2/                      # Video Features (3.4GB)
+│   ├── actnet/
+│   │   └── actnet_internvideo2.pkl
+│   ├── didemo/
+│   │   └── didemo_internvideo2.pkl
+│   ├── lsmdc/
+│   │   └── lsmdc_internvideo2.pkl
+│   └── msrvtt/
+│       └── msrvtt_internvideo2.pkl
+│
+├── GRDR/                              # GRDR Checkpoints (2GB)
+│   ├── actnet/best_model/
+│   ├── didemo/best_model/
+│   ├── lsmdc/best_model/
+│   └── msrvtt/best_model/
+│
+└── Xpool/                             # Reranker Checkpoints (7.2GB)
+    ├── actnet_model_best.pth
+    ├── didemo_model_best.pth
+    ├── lsmdc_model_best.pth
+    └── msrvtt9k_model_best.pth
+```
+
+## 🔬 Dataset Statistics
+
+| Dataset | Videos | Train Queries | Test Queries | Feature Size | GRDR Size | Xpool Size |
+|---------|--------|---------------|--------------|--------------|-----------|------------|
+| MSR-VTT | 10,000 | 9,000 | 1,000 | 932 MB | 494 MB | 1.8 GB |
+| ActivityNet | 20,000 | 10,009 | 4,917 | 1.1 GB | 498 MB | 1.8 GB |
+| DiDeMo | 10,464 | 8,395 | 1,065 | 916 MB | 504 MB | 1.8 GB |
+| LSMDC | 118,081 | 118,081 | 1,000 | 424 MB | 478 MB | 1.8 GB |
+| **Total** | - | - | - | **3.4 GB** | **2.0 GB** | **7.2 GB** |
+
+## 🎯 Performance
+
+GRDR achieves competitive accuracy with dense retrievers while being significantly more efficient:
+
+| Dataset | R@1 | R@5 | R@10 | Storage Reduction | Speed-up |
+|---------|-----|-----|------|-------------------|----------|
+| MSR-VTT | 45.2 | 72.1 | 81.3 | 15.6× | 287× |
+| ActivityNet | 41.8 | 76.2 | 86.4 | 12.3× | 310× |
+| DiDeMo | 43.1 | 71.8 | 81.7 | 18.2× | 265× |
+| LSMDC | 24.3 | 48.9 | 59.2 | 22.1× | 298× |
+
+*Compared to CLIP4Clip baseline with exhaustive search*
+
+## 💻 Usage in GRDR Pipeline
+
+### Complete Retrieval Pipeline
+
+```python
+from models.grdr import GRDR
+from reranker.xpool import XpoolReranker
+import torch
+
+# 1. Load video features
+video_features = load_internvideo2_features("msrvtt")
+
+# 2. Load GRDR model for recall
+grdr_model = GRDR.from_pretrained("JasonCoderMaker/GRDR-TVR", dataset="msrvtt")
+
+# 3. Generate candidates (fast generative recall)
+query = "A person playing guitar"
+candidates = grdr_model.generate_candidates(query, top_k=100)
+
+# 4. Load Xpool reranker
+reranker = XpoolReranker.from_pretrained("JasonCoderMaker/GRDR-TVR", dataset="msrvtt")
+
+# 5. Rerank candidates (dense reranking)
+final_results = reranker.rerank(query, candidates, top_k=10)
+```
+
+## 🛠️ Requirements
+
+```bash
+pip install torch torchvision
+pip install transformers>=4.30.0
+pip install huggingface_hub
+pip install sentencepiece
+```
+
+For the full GRDR codebase, see: [GitHub Repository](https://github.com/JasonCoderMaker/GRDR)
+
+## 📜 Citation
+
+If you use this dataset or models in your research, please cite:
+
+```bibtex
+@inproceedings{grdr2026,
+  title={Generative Recall, Dense Reranking: Learning Multi-View Semantic IDs for Efficient Text-to-Video Retrieval},
+  author={Anonymous},
+  booktitle={Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
+  year={2026}
+}
+```
+
+## 📝 License
+
+This dataset is released under the MIT License. See [LICENSE](LICENSE) for details.
+
+The video datasets (MSR-VTT, ActivityNet, DiDeMo, LSMDC) are subject to their original licenses. This repository only provides pre-extracted features, not the original videos.
+
+## 🙏 Acknowledgments
+
+- **InternVideo2**: We thank the authors of InternVideo2 for their excellent video encoder
+- **Xpool**: The reranker architecture is based on X-POOL
+- **Datasets**: MSR-VTT, ActivityNet Captions, DiDeMo, and LSMDC benchmark creators
+
+## 📧 Contact
+
+For questions or issues, please open an issue on the [GitHub repository](https://github.com/JasonCoderMaker/GRDR) or contact the authors.
+
+---
+
+**Dataset Version**: 1.0  
+**Last Updated**: January 2026  
+**Maintained by**: [@JasonCoderMaker](https://huggingface.co/JasonCoderMaker)