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

arXiv 2404.08936 ↗First posted 13 Apr 2024cs.CVcited by 0

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Abstract (arXiv)

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 (CS3Net), 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 CS3Net, 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 CS3Net 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 33 of its 66 references that are in the map. First posted on arXiv 13 Apr 2024.

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33 of 66 references.

References · 33

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21 more
  1. 2021Camouflaged Object Segmentation with Distraction Miningcited by 151
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  21. 2021Pedestrian Attribute Recognition in Video Surveillance Scenarios Based on View-attribute Attention Localizationcited by 8

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