Win-Win Fusion: Multi-Modal Image Fusion with Visual-Semantic Collaboration
Existing multi-modal image fusion (MMIF) methods generally face an optimization gap between the visual and semantic domains, limiting the enhancement of perception capabilities.
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Key points
- To overcome this long-standing barrier, this work proposes W2F, a new visual-semantic mutual reinforcing paradigm for MMIF.
- Specifically, the segmentation-guided fusion loss introduces object-aware structural guidance into visual fusion, while the embedded information restoration prior strengthens the ability of visual fusion to provide reliable perceptual evidence for the semantic branch under degraded conditions.
- Then, we introduce a strong multi-modal information interaction mechanism composed of interactive gated mixed attention (IGM-Att) and progressive coupling attention (PC-Att).
- Finally, we construct a collaborative mechanism to couple visual fusion and semantic fusion.
Sources (1)
- [1]Win-Win Fusion: Multi-Modal Image Fusion with Visual-Semantic CollaborationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 02:22 AM
Existing multi-modal image fusion (MMIF) methods generally face an optimization gap between the visual and semantic domains, limiting the enhancement of perception capabilities.
To overcome this long-standing barrier, this work proposes W2F, a new visual-semantic mutual reinforcing paradigm for MMIF.
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