A Revisit to the Decoder for Camouflaged Object Detection
This work addresses the challenge of detecting camouflaged objects in images, which is important for applications like surveillance and biology, but is incremental as it builds on existing decoding strategies.
The paper tackles the problem of camouflaged object detection by proposing a novel decoder architecture with Enrich and Retouch Decoders to improve segmentation accuracy, demonstrating superior performance across various encoders in experiments.
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.