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arXiv · 2404.08936

Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage

Yang Hu·Jinxia Zhang·Kaihua Zhang·Yin Yuan·Jiale Huang·Zechao Zhan·Xing Wang
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Abstract

Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial computational costs. To address this, we propose the Co-Supervised Spotlight Shifting Network (CS$^3$Net), a compact single-branch framework inspired by how shifting light source exposes camouflage. Our spotlight shifting strategy replaces multi-branch designs by generating supervisory signals that highlight boundary cues. Within CS$^3$Net, a Projection Aware Attention (PAA) module is devised to strengthen feature extraction, while the Extended Neighbor Connection Decoder (ENCD) enhances final predictions. Extensive experiments on public datasets demonstrate that CS$^3$Net not only achieves superior performance, but also reduces Multiply-Accumulate operations (MACs) by 32.13% compared to state-of-the-art COD methods, striking an optimal balance between efficiency and effectiveness.

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Yang Hu, Jinxia Zhang, Kaihua Zhang, Yin Yuan, Jiale Huang, Zechao Zhan, Xing Wang. 2024-04-13. Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage. https://arxiv.org/abs/2404.08936

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