---
license: mit
task_categories:
- visual-document-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**


[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)

### 4. Xpool Video Features (~3.2GB)
Pre-extracted CLIP video features for Xpool reranker:
- **MSR-VTT**: 235MB
- **ActivityNet**: 351MB
- **DiDeMo**: 221MB
- **LSMDC**: 2.4GB

**Reranker Details:**
- Architecture: CLIP-based (ViT-B/32)
- Purpose: Fine-grained reranking of recalled candidates
- Format: PyTorch checkpoint files (`.pth`)

## 📂 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
```

## 📝 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


**Dataset Version**: 1.0  
**Last Updated**: January 2026  
**Maintained by**: [@JasonCoderMaker](https://huggingface.co/JasonCoderMaker)