Frequency Representation Integration for Camouflaged Object Detection

Abstract

Recent camouflaged object detection (COD) approaches have been proposed to accurately segment objects blended into surroundings. The most challenging and critical issue in COD is to find out the lines of demarcation between objects and background in the camouflage environment. Because of the similarity between the target object and the background, these lines are difficult to be found accurately. However, these are easy to be observed in different frequency components of the image. To this end, in this paper we rethink COD from the perspective of frequency components and propose a Frequency Representation Integration Network to mine informative cues from them. Specifically, we obtain high-frequency components from the original image by Laplacian pyramid-like decomposition, and then respectively send the image to a transformer-based encoder and frequency components to a tailored CNN-based Residual Frequency Array Encoder. Besides, we utilize the multi-head self-attention in transformer encoder to capture low-frequency signals, which can effectively parse the overall contextual information of camouflage scenes. We also design a Frequency Representation Reasoning Module, which progressively eliminates discrepancies between differentiated frequency representations and integrates them by modeling their point-wise relations. Moreover, to further bridge different frequency representations, we introduce the image reconstruction task to implicitly guide their integration. Sufficient experiments on three widely-used COD benchmark datasets demonstrate that our method surpasses existing state-of-the-art methods by a large margin.