Efficient and accurate camouflaged object detection (COD) poses a challenge
in the field of computer vision. Recent approaches explored the utility of edge
information for network co-supervision, achieving notable advancements.
However, these approaches introduce an extra branch for complex edge
extraction, complicate the model architecture and increases computational
demands. Addressing this issue, our work replicates the effect that animal's
camouflage can be easily revealed under a shifting spotlight, and leverages it
for network co-supervision to form a compact yet efficient single-branch
network, the Co-Supervised Spotlight Shifting Network (CS$^3$Net). The
spotlight shifting strategy allows CS$^3$Net to learn additional prior within a
single-branch framework, obviating the need for resource demanding multi-branch
design. To leverage the prior of spotlight shifting co-supervision, we propose
Shadow Refinement Module (SRM) and Projection Aware Attention (PAA) for feature
refinement and enhancement. To ensure the continuity of multi-scale features
aggregation, we utilize the Extended Neighbor Connection Decoder (ENCD) for
generating the final predictions. Empirical evaluations on public datasets
confirm that our CS$^3$Net offers an optimal balance between efficiency and
performance: it accomplishes a 32.13% reduction in Multiply-Accumulate (MACs)
operations compared to leading efficient COD models, while also delivering
superior performance.

基于移动聚光灯的单支网络，在效率和性能之间取得了最佳平衡，比领先的高效 COD 模型减少了 32.13% 的 MAC 运算，并且在性能上表现出色。