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}{{this https URL}}.

本文提出了自适应引导学习网络（AGLNet），它是一种统一的可学习模型，用于在 CNN 模型中探索和调整不同的附加线索，以引导准确的伪装特征学习，并在不同的附加线索下实现了显著的性能改进。