A Revisit to the Decoder for Camouflaged Object Detection


Seung Woo Ko (LG AI Research), Joopyo Hong (Seoul National University), Suyoung Kim (Seoul National University), Seungjai Bang (Seoul National University), Sungzoon Cho (Seoul National University), Nojun Kwak (Seoul National University), Hyung-Sin Kim (Seoul National University), Joonseok Lee (Seoul National University)
The 35th British Machine Vision Conference
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Abstract

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.

Citation

@inproceedings{Ko_2024_BMVC,
author    = {Seung Woo Ko and Joopyo Hong and Suyoung Kim and Seungjai Bang and Sungzoon Cho and Nojun Kwak and Hyung-Sin Kim and Joonseok Lee},
title     = {A Revisit to the Decoder for Camouflaged Object Detection},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0199.pdf}
}


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