arXiv Computer Vision
Aug 25

HP-UniIF: Hierarchical Prompt Learning for Unified Image Fusion

HP-UniIF is a unified vision framework that uses diffusion priors and a depth‑wise hierarchical conditional modulation strategy to support heterogeneous image fusion, visual restoration, and downstream perception tasks. The framework introduces task prompt modulation at bottleneck layers, a degradation prompt router at shallow layers, and an application prompt bank at decoding stages to decouple and adapt to different objectives. Experiments across multiple fusion tasks, degradations, and downstream applications show that HP‑UniIF achieves superior performance while maintaining visually faithful results and task‑relevant semantics.

By Xingxin Xu, Siqi Zhao, Xin Li, Xinjie Yao, Yiming Sun, Pengfei Zhu
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
arXiv Computer Vision
Sep 2

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

The paper introduces IT-TextFusion, an iterative text-guided image fusion framework that uses text-conditioned feature interaction across multiple fusion and refinement stages. It incorporates deep cross-attention, multi-scale cross-gate fusion, and stage-specific text-conditioned modulation to enable degradation-aware global semantic conditioning while preserving complementary visible and infrared information. Experiments on benchmark datasets demonstrate improvements in information-preservation and perceptual-quality metrics, with some metric-dependent trade-offs.

By Siyang Liu, Peiyi Zhou, Tianle Jin, Rongrong Bian, Zheke Jin, Mengze Gao
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.

arXiv Computer Vision
3d ago

FuseAdapt: Parameter-Efficient Multimodal Fusion for Semantic Segmentation with Missing Modalities

FuseAdapt is a parameter‑efficient framework for multimodal semantic segmentation that performs fusion within the adaptation space of a frozen vision foundation model (VFM). Instead of a separate fusion network, each modality receives a lightweight adaptation branch, and only low‑rank residual activations are routed across modalities inside the frozen backbone. The method also introduces Predictive Modality Modeling (PMM) to handle missing modalities during training, achieving significant mIoU gains on three benchmarks.

By Xin Zhang, Robby T. Tan
arXiv Computer Vision
Sep 24

Diff-RF: Mutually Reinforced Image Registration and Fusion via Degradation-Aware Learning

Diff‑RF is a diffusion‑based framework that jointly performs image registration and fusion while accounting for degradation in multi‑modal images. It first restores modality‑specific degradations within each image, then uses a cross‑modal diffusion module that couples registration and fusion, refining alignment and enhancing complementary information. Experiments on extended datasets show that this coupled approach yields higher registration accuracy and fusion quality under diverse degraded conditions.

By Xunpeng Yi, Zaixi Du, Qinglong Yan, Yibing Zhang, Han Xu, Jiayi Ma