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About this paper
Weichao Cai, Weiliang Huang, Biao Xue, Chao Huang, Fei Yuan, Bob Zhang
A multi-task complementary learning framework and a new dataset address robust maritime image perception, integrating restoration, fusion, and segmentation for enhanced understanding in degraded conditions. The framework achieved an mIoU of 60.2% on the newly introduced Infrared-Visible Maritime Ship Dataset (IVMSD) and demonstrated improved robustness and perceptual quality.
Key insights
- Unified end-to-end optimization of low-level visual processing (restoration and fusion) and high-level semantic understanding (segmentation) can synergistically improve overall perception performance in degraded environments.
- Jointly modeling characteristics in both frequency and spatial domains (FSEC module) is effective for mitigating diverse and coupled maritime image degradations.
- Gated cross-modal attention and semantic-guided enhancement mechanisms (GMIA and SVCA) enable selective feature integration, suppress noise propagation, and align visual processing with task-relevant semantic objectives.
Summary by alphaXiv.
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