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arXiv Computer Vision By Lin Hong, Chenhui Wang, Linan Deng, Yuning Cui, Yu Zhang, Xin Wang, Bojian Zhang, Xingchen Yang, Fumin Zhang

Learning Spatially Adaptive Structural Coordination for Underwater Salient Object Detection

Read the original on arXiv Computer Vision →

The paper introduces SASC-USOD, a framework for underwater salient object detection that learns spatially adaptive coordination between two structural representations: a boundary-sensitive representation using Laplacian filtering and a region-coherent representation via dual-range anisotropic large-kernel aggregation. A spatial coordination module estimates the relative reliability of these representations and adaptively blends them based on image content. Experiments on USOD10K and USOD benchmarks show that SASC-USOD outperforms existing methods, reducing MAE by 4.07% and 23.53% respectively, and its lightweight variant achieves 21 FPS on an NVIDIA Jetson TX2 NX.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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