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