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IT-TextFusion: Iterative Text-Image Interaction with Text-Guided Residual Refinement for Degradation-Aware Image Fusion — arXiv:2609.01092

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.01092
License (as archived): CC0-1.0
Archived: 2026-10-17T07:13:47.750Z
SHA-256 of the archived text: 12b8f3042ba3ba2c3f1685b911a57b256229559535c3e1d401369f416b8fd9dd
Stable URL of this page: https://metraipolis.com/documento/dc_8e56bb08bbde

How to cite

IT-TextFusion: Iterative Text-Image Interaction with Text-Guided Residual Refinement for Degradation-Aware Image Fusion — arXiv:2609.01092 — arXiv (en), https://arxiv.org/abs/2609.01092 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_8e56bb08bbde (Metraipolis archive dc_8e56bb08bbde, sha256 12b8f3042ba3ba2c3f1685b911a57b256229559535c3e1d401369f416b8fd9dd)

BibTeX

@misc{dc8e56bb08bbde,
  title = {IT-TextFusion: Iterative Text-Image Interaction with Text-Guided Residual Refinement for Degradation-Aware Image Fusion — arXiv:2609.01092},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.01092},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_8e56bb08bbde; sha256 12b8f3042ba3ba2c3f1685b911a57b256229559535c3e1d401369f416b8fd9dd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.01092. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Text-guided image fusion has recently emerged as an effective paradigm for integrating multi-modal information while enabling flexible and task-oriented fusion control. However, existing text-guided fusion methods often rely on shallow semantic-visual interaction and limited attention mechanisms, which restrict their ability to robustly handle complex degradations and fully exploit textual guidance. In this paper, we propose an iterative text-guided image fusion framework that incorporates text-conditioned feature interaction across multiple fusion and refinement stages. The proposed method integrates deepest-level Cross-Attention, multi-scale Cross-Gate Fusion, and stage-specific text-conditioned modulation, allowing the global text embedding to condition hierarchical feature fusion and residual refinement. By repeatedly injecting the pooled text embedding across hierarchical decoder and refinement stages, the proposed framework provides degradation-aware global semantic conditioning while preserving complementary information from the visible and infrared modalities. Experiments on several benchmark datasets show that the proposed method improves several information-preservation and perceptual-quality metrics, while exhibiting metric-dependent trade-offs on some datasets.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-02 in cs.CV).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2609.01092 — 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 2026-10-17T07:00:58.963Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-09-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).