Aug 2026· IEEE Transactions on Pattern Analysis and Machine Intelligence· Vol PP, pp. 1-18· 1 citation
Medicine
TL;DR
An innovative degradation-aware image fusion framework via leveraging text-driven semantics, termed GText-IF, which can perform effective degradation-aware fusion in real degraded image scenarios, exhibiting remarkable advancement, robustness, and practicality.
This paper proposes an iterative text-guided image fusion framework that incorporates text-conditioned feature interaction across multiple fusion and refinement stages, and provides degradation-aware global semantic conditioning while preserving complementary information from the visible and infrared modalities.
Si-Yang Liu, Pei Zhou, Tianhu Jin et al.· 0 citations
This paper systematically sorts out mainstream T2I technologies into four main paradigms: early fusion, late fusion, attention mechanism-based fusion, and hybrid fusion, and systematically explains their core mechanisms, evolution trends, and conducts model performance comparisons.
Tian Shi· Applied and Computational En...· 1 citation
Infrared–visible image fusion aims to combine thermal saliency from infrared imagery with the structural detail preserved in the visible spectrum. In existing fusion methods, the two modalities are fused through fixed channel mixing, the same text embedding is reused across decoder scales, and explicit cross-modal inte...
A new ITM framework that improves the model's discriminative performance by focusing on localized core attributes and has a better robustness in handling highly similar hard negatives, which provides a new way to address cross-modal hard sample discrimination.
Ya-Ting Liu, Kuang-Rong Hao· International Conference on...· 0 citations
A multi-dimensional image quality assessment framework for T2I tasks that can not only distinguish overall performance differences across models, but also provide interpretable results at the level of individual dimensions is proposed.
The proposed UniDiffFusion framework is trained in a progressive manner to decouple fusion learning, degradation-aware restoration, and application-specific adaptation, thereby reducing interference among heterogeneous objectives.
Xing-Xin Xu, Si-Qi Zhao, Xin Li et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
