SGPDFuse: Semantically-Guided Physics-Disentanglement General Multi-Modal Image Fusion
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
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.
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.
RA‑SOD is a new RGB‑Thermal salient object detection framework that explicitly models the reliability of each modality. It introduces a reliability‑conditioned representation, an uncertainty‑guided dual‑stream refinement, and a pixel‑wise modality competition mechanism to adaptively compensate degraded features and suppress unreliable evidence. Experiments on four benchmarks show that RA‑SOD achieves state‑of‑the‑art performance and remains robust under severe modality degradation.
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.
arXiv:2608.21786v2 Announce Type: replace Abstract: General image fusion aims to integrate complementary information from multiple source images, but existing methods often rely on task-specific mode...
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.