Hugging Face Trending Papers
Jul 26

ConFusion: Continuous Fusion Space Learning for Fine-Grained Controllable Infrared and Visible Image Fusion

Controllable infrared-visible image fusion aims to integrate complementary thermal and structural information with flexible region-aware modulation, producing fused images that adapt to diverse user requirements and downstream tasks. However, existing methods typically rely on predefined discrete control conditions, leading to a sparse space that fails to support fine-grained modulation demands.

arXiv Computer Vision
Oct 12

Win-Win Fusion: Multi-Modal Image Fusion with Visual-Semantic Collaboration

The paper introduces W2F, a visual‑semantic mutual‑reinforcing framework for multi‑modal image fusion (MMIF). It enhances feature complementarity with a segmentation‑guided fusion loss and an embedded information restoration prior, and employs interactive gated mixed attention (IGM‑Att) and progressive coupling attention (PC‑Att) to fuse local and global information. A collaborative mechanism with a feature search module and gradient correction further aligns visual and semantic fusion, leading to state‑of‑the‑art visual appeal and decision accuracy.

By Hao Zhang, Xuhui Zuo, Jiayi Ma
Hugging Face Trending Papers
Aug 13

P2Fusion: Prompt-based Progressive Infrared-Visible Image Fusion via Dual-Prior Distillation

Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.