You Do Not Need Additional Priors in Camouflage Object Detection
Yuchen Dong Heng Zhou Chengyang Li Junjie Xie Yongqiang Xie Zhongbo Li
Camouflage object detection (COD) poses a significant challenge due to the
high resemblance between camouflaged objects and their surroundings. Although
current deep learning methods have made significant progress in detecting
camouflaged objects, many of them heavily rely on additional prior information.
However, acquiring such additional prior information is both expensive and
impractical in real-world scenarios. Therefore, there is a need to develop a
network for camouflage object detection that does not depend on additional
priors. In this paper, we propose a novel adaptive feature aggregation method
that effectively combines multi-layer feature information to generate guidance
information. In contrast to previous approaches that rely on edge or ranking
priors, our method directly leverages information extracted from image features
to guide model training. Through extensive experimental results, we demonstrate
that our proposed method achieves comparable or superior performance when
compared to state-of-the-art approaches.