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

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

@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}
}