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README.md
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---
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license: cc-by-4.0
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tags:
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- multimodal-retrieval
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- text-video-retrieval
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- audio-visual
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- hypergraph
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---
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# HyVol: Hypergraph-Regularized Gramian Volumes for Multimodal Retrieval
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Pretrained weights for the WACV 2027 paper
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**Hypergraph-Regularized Gramian Volumes for Multimodal Retrieval**
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by Anindya Nag, Ambuj Mehrish and Sebastiano Vascon.
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Paper: [arXiv:2609.15320](https://arxiv.org/abs/2609.15320)
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HyVol is a training-only module for volume-based multimodal retrieval. A hypergraph
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adds semantic relations between training samples before the volume loss is computed.
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The hypergraph is removed after training, so the backbone, scoring function and
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retrieval cost are unchanged.
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## Checkpoints
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| File | Backbone |
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|---|---|
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| `GRAM_HYVOL/HyVol_GRAM.pt` | GRAM + HyVol |
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| `HyperGRAM_HYVOL/HyVol_HyperGRAM.pt` | Hyperbolic GRAM + HyVol |
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Both models are trained on a 150K-clip subset of VAST-27M and evaluated zero-shot.
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Load each checkpoint with the codebase of its backbone (GRAM or Hyperbolic GRAM).
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## Citation
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```bibtex
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@inproceedings{nag2027hyvol,
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title = {Hypergraph-Regularized Gramian Volumes for Multimodal Retrieval},
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author = {Nag, Anindya and Mehrish, Ambuj and Vascon, Sebastiano},
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booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
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year = {2027}
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}
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```
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