提出双模块解码器，提升隐蔽目标分割精度。
A Revisit to the Decoder for Camouflaged Object Detection
- 用通道注意力增强关键特征，改善信息提取
- 通过空间注意力精修边界，生成更精细分割图
- 适用于多种编码器，对边界敏感任务有优势
隐蔽目标检测（COD）旨在生成隐藏在背景中的隐蔽物体的细粒度分割图。由于隐蔽物体特性隐匿，解码器需针对性地提取其有效特征并谨慎生成复杂边界。本文提出新型架构，在主流解码策略基础上引入增强解码器（Enrich Decoder）与修整解码器（Retouch Decoder），以生成更精细的分割结果。其中，增强解码器利用通道注意力放大对COD至关重要的特征通道；修整解码器则通过空间注意力聚焦关键像素（如边界区域），进一步优化分割图。大量实验表明，所提方法（ENTO）在多种编码器下均表现优越，两个组件各司其职、相互补充。
原文摘要 · Abstract (English)
Camouflaged object detection (COD) aims to generate a fine-grained segmentation map of camouflaged objects hidden in their background. Due to the hidden nature of camouflaged objects, it is essential for the decoder to be tailored to effectively extract proper features of camouflaged objects and extra-carefully generate their complex boundaries. In this paper, we propose a novel architecture that augments the prevalent decoding strategy in COD with Enrich Decoder and Retouch Decoder, which help to generate a fine-grained segmentation map. Specifically, the Enrich Decoder amplifies the channels of features that are important for COD using channel-wise attention. Retouch Decoder further refines the segmentation maps by spatially attending to important pixels, such as the boundary regions. With extensive experiments, we demonstrate that ENTO shows superior performance using various encoders, with the two novel components playing their unique roles that are mutually complementary.
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