Adaptive Guidance Learning for Camouflaged Object Detection

• URL: http://arxiv.org/abs/2405.02824v2
• Date: Tue, 7 May 2024 02:17:59 GMT
• Title: Adaptive Guidance Learning for Camouflaged Object Detection
• Authors: Zhennan Chen, Xuying Zhang, Tian-Zhu Xiang, Ying Tai,
• Abstract summary: This paper proposes an adaptive guidance learning network, dubbed textitAGLNet, to guide accurate camouflaged feature learning. Experiments on three widely used COD benchmark datasets demonstrate that the proposed method achieves significant performance improvements.
• Score: 23.777432551429396
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Camouflaged object detection (COD) aims to segment objects visually embedded in their surroundings, which is a very challenging task due to the high similarity between the objects and the background. To address it, most methods often incorporate additional information (e.g., boundary, texture, and frequency clues) to guide feature learning for better detecting camouflaged objects from the background. Although progress has been made, these methods are basically individually tailored to specific auxiliary cues, thus lacking adaptability and not consistently achieving high segmentation performance. To this end, this paper proposes an adaptive guidance learning network, dubbed \textit{AGLNet}, which is a unified end-to-end learnable model for exploring and adapting different additional cues in CNN models to guide accurate camouflaged feature learning. Specifically, we first design a straightforward additional information generation (AIG) module to learn additional camouflaged object cues, which can be adapted for the exploration of effective camouflaged features. Then we present a hierarchical feature combination (HFC) module to deeply integrate additional cues and image features to guide camouflaged feature learning in a multi-level fusion manner.Followed by a recalibration decoder (RD), different features are further aggregated and refined for accurate object prediction. Extensive experiments on three widely used COD benchmark datasets demonstrate that the proposed method achieves significant performance improvements under different additional cues, and outperforms the recent 20 state-of-the-art methods by a large margin. Our code will be made publicly available at: \textcolor{blue}{{https://github.com/ZNan-Chen/AGLNet}}.

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