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--- |
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license: mit |
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task_categories: |
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- visual-document-retrieval |
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- text-to-video |
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language: |
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- en |
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tags: |
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- video-retrieval |
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- generative-retrieval |
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- semantic-ids |
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- text-to-video |
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size_categories: |
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- 10K<n<100K |
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--- |
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# GRDR-TVR: Generative Recall, Dense Reranking for Text-to-Video Retrieval |
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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). |
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## 📄 Paper |
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**Generative Recall, Dense Reranking: Learning Multi-View Semantic IDs for Efficient Text-to-Video Retrieval** |
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[Paper PDF](https://arxiv.org/abs/XXXX.XXXXX) | [Code Repository](https://github.com/JasonCoderMaker/GRDR) |
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## 📊 Dataset Overview |
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This dataset includes three main components: |
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### 1. InternVideo2 Features (~3.4GB) |
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Pre-extracted video features using InternVideo2 encoder for four benchmark datasets: |
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- **MSR-VTT**: 10,000 videos (932MB) |
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- **ActivityNet**: 20,000 videos (1.1GB) |
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- **DiDeMo**: 10,464 videos (916MB) |
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- **LSMDC**: 1,000 movies, 118,081 clips (424MB) |
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**Feature Details:** |
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- Dimension: 512-d embeddings |
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- Format: Pickle files (`.pkl`) with `{video_id: embedding}` mappings |
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- Extraction: InternVideo2 (InternVL-2B) with temporal pooling |
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### 2. GRDR Model Checkpoints (~2GB) |
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Trained GRDR models (T5-small based) for all four datasets: |
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- **MSR-VTT**: 494MB |
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- **ActivityNet**: 498MB |
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- **DiDeMo**: 504MB |
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- **LSMDC**: 478MB |
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**Checkpoint Components:** |
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- `best_model.pt` - Complete model checkpoint |
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- `best_model.pt.model` - T5 encoder-decoder weights |
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- `best_model.pt.videorqvae` - Video RQ-VAE quantizer |
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- `best_model.pt.code` - Pre-computed semantic IDs |
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- `best_model.pt.centroids` - Codebook centroids |
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- `best_model.pt.embedding` - Learned embeddings |
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- `best_model.pt.start_token` - Start token embeddings |
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**Model Architecture:** |
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- Base: T5-small (60M parameters) |
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- Codebook size: 128/96/200 (dataset-dependent) |
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- Max code length: 3 |
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- Training: 3-phase progressive training |
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### 3. Xpool Reranker Checkpoints (~7.2GB) |
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Pre-trained reranker models for dense reranking stage: |
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- **MSR-VTT**: msrvtt9k_model_best.pth (1.8GB) |
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- **ActivityNet**: actnet_model_best.pth (1.8GB) |
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- **DiDeMo**: didemo_model_best.pth (1.8GB) |
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- **LSMDC**: lsmdc_model_best.pth (1.8GB) |
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### 4. Xpool Video Features (~3.2GB) |
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Pre-extracted CLIP video features for Xpool reranker: |
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- **MSR-VTT**: 235MB |
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- **ActivityNet**: 351MB |
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- **DiDeMo**: 221MB |
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- **LSMDC**: 2.4GB |
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**Reranker Details:** |
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- Architecture: CLIP-based (ViT-B/32) |
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- Purpose: Fine-grained reranking of recalled candidates |
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- Format: PyTorch checkpoint files (`.pth`) |
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## 📂 Repository Structure |
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``` |
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GRDR-TVR/ |
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├── README.md # This file |
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├── download_features.py # Python download utility |
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├── download_checkpoints.sh # Bash download script |
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│ |
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├── InternVideo2/ # Video Features (3.4GB) |
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│ ├── actnet/ |
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│ │ └── actnet_internvideo2.pkl |
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│ ├── didemo/ |
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│ │ └── didemo_internvideo2.pkl |
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│ ├── lsmdc/ |
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│ │ └── lsmdc_internvideo2.pkl |
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│ └── msrvtt/ |
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│ └── msrvtt_internvideo2.pkl |
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│ |
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├── GRDR/ # GRDR Checkpoints (2GB) |
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│ ├── actnet/best_model/ |
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│ ├── didemo/best_model/ |
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│ ├── lsmdc/best_model/ |
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│ └── msrvtt/best_model/ |
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│ |
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└── Xpool/ # Reranker Checkpoints (7.2GB) |
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├── actnet_model_best.pth |
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├── didemo_model_best.pth |
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├── lsmdc_model_best.pth |
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└── msrvtt9k_model_best.pth |
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``` |
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## 📝 License |
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This dataset is released under the MIT License. See [LICENSE](LICENSE) for details. |
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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. |
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## 🙏 Acknowledgments |
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- **InternVideo2**: We thank the authors of InternVideo2 for their excellent video encoder |
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- **Xpool**: The reranker architecture is based on X-POOL |
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- **Datasets**: MSR-VTT, ActivityNet Captions, DiDeMo, and LSMDC benchmark creators |
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**Dataset Version**: 1.0 |
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**Last Updated**: January 2026 |
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**Maintained by**: [@JasonCoderMaker](https://huggingface.co/JasonCoderMaker) |
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