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 | Code Repository

📊 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 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