---
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)