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
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
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
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.
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
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
Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degradation, disrupting feature representation and requiring simultaneous feature restoration and cross-modal complementarity.
CoMLP introduces a cooperatively-gated MLP module that fuses multimodal medical data—such as imaging modalities and clinical reports—without relying on computationally heavy cross-attention. The module uses regional and dilated MLP interactions to capture both local and global cross-modal dependencies, enabling fine-grained fusion at high spatial resolutions. Experiments on five segmentation benchmarks, covering 2D/3D images and diverse anatomical regions, show consistent improvements over state-of-the-art multi-modal and language-guided methods, highlighting the effectiveness of MLP-based interaction for medical image segmentation.
By Mingyuan Meng, Shuchang Ye, Mingjian Li, Zhenyu Zhao, Jinman Kim, Lei Bi
Multimodal fusion learning (MFL) has shown great potential in the medical domain, where we are faced with disparate data modalities such as imaging, clinical records, and omics. However, existing MFL strategies face several major challenges.
The paper introduces FlexibleFusion, a method for infrared-visible object detection that adapts to both complete and missing-modality scenarios. It employs a Modality-Aware Experts Collaboration mechanism to selectively fuse cross-modal or intra-modal pathways, and a Residual Self-Paced Entropic Optimal Transport module to align heterogeneous feature distributions without heavy optimization. Experiments demonstrate consistent performance across various modality configurations.
By Yue Zhao, Hua Yu, Yukun Zhao, Yuzhi Zhang, Maoguo Gong, Xin Mei, Zhuping Hu, Yanchi Li, A. K. Qin
arXiv:2609.31170v1 Announce Type: new
Abstract: Task-driven image restoration aims to improve both image quality and downstream task performance. However, existing methods predominantly focus on sing...
By Yanjie Tu, Qingsen Yan, Axi Niu, Wenxuan Cai, Tao Hu, Wei Dong, Haokui Zhang
arXiv:2608.29220v1 Announce Type: new
Abstract: Multimodal image fusion (MMIF) aims to integrate complementary sensor data into a single representation that preserves intrinsic scene reality while el...
By Haozhen Wei, Chengjun Jiang, Yutong Guo, Xinrui Ju, Xingyuan Li, Xiang Chen, Jinyuan Liu