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ProtoHGF-Net: Prototype HyperGraph Fusion with Intra-modal Calibration for RGBT Object Detection — arXiv:2608.11595

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.11595
License (as archived): CC0-1.0
Archived: 2026-10-19T05:49:22.312Z
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How to cite

ProtoHGF-Net: Prototype HyperGraph Fusion with Intra-modal Calibration for RGBT Object Detection — arXiv:2608.11595 — arXiv (en), https://arxiv.org/abs/2608.11595 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c8b2cbda0510 (Metraipolis archive dc_c8b2cbda0510, sha256 fc375add8b60fe5b17f3bb2b348d0af10c3352ef5a8c5481018863f31b45c07b)

BibTeX

@misc{dcc8b2cbda0510,
  title = {ProtoHGF-Net: Prototype HyperGraph Fusion with Intra-modal Calibration for RGBT Object Detection — arXiv:2608.11595},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.11595},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c8b2cbda0510; sha256 fc375add8b60fe5b17f3bb2b348d0af10c3352ef5a8c5481018863f31b45c07b},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.11595. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

RGB-Thermal (RGBT) object detection enables robust perception in complex scenes by leveraging the complementary strengths of visible textures and thermal cues. However, existing methods mainly rely on dense cross-modal interactions over full-resolution features, which inevitably introduce background interference and hinder the learning of target-relevant representations. In this paper, we propose the Prototype HyperGraph Fusion Network (ProtoHGF-Net), a novel framework that redefines cross-modal fusion as prototype-level semantic interaction rather than the dense cross-modal interaction paradigm. Specifically, we design Prototype HyperGraph Fusion to perform cross-modal interaction in a compact prototype-level semantic space. This design enables more selective fusion among target-relevant prototypes. To support this prototype-level fusion, we propose Teacher-Mask Calibration Distillation, which calibrates modality features before fusion using modality-specific teachers and target-aware masks. This strategy suppresses backgrou- nd-dominant responses and produces more target-focused features. Extensive experiments on DroneVehicle, DVTOD, and FLIR demonstrate that ProtoHGF-Net achieves state-of-the-art performance with 85.9\% $mAP_{50}$, 88.2\% $mAP_{50}$, and 79.1\% $mAP_{50}$, respectively. Our code is available at \href{ this https URL }{GitHub}.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-13 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2608.11595 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T05:42:25.868Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-08-13?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).