metadata
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
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}
}