Datasets:
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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`)
## 📂 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)
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