CGCOD: Class-Guided Camouflaged Object Detection
Chenxi Zhang, Qing Zhang, Jiayun Wu, Youwei Pang
Abstract
Camouflaged Object Detection (COD) aims to identify objects that blend seamlessly into their surroundings. The inherent visual complexity of camouflaged objects, including their low contrast with the background, diverse textures, and subtle appearance variations, often obscures semantic cues, making accurate segmentation highly challenging. Existing methods primarily rely on visual features, which are insufficient to handle the variability and intricacy of camouflaged objects, leading to unstable object perception capability and ambiguous segmentation results. To tackle these limitations, we introduce a novel COD task, class-guided camouflaged object detection (CGCOD), which extends the conventional COD task by incorporating object-specific class knowledge to enhance detection robustness and accuracy. To facilitate this task, we present a new dataset, CamoClass, comprising camouflaged objects with class annotations. Furthermore, we propose a multi-stage framework, CGNet, which incorporates a plug-and-play class prompt generator and a simple yet effective class-guided detector. This establishes a new paradigm for COD, bridging the gap between contextual understanding and class-guided detection. Extensive experimental results demonstrate the effectiveness of our flexible framework in improving the performance of proposed and existing detectors by leveraging class-level textual information. The Camoclass dataset and the corresponding source code will be made publicly available upon acceptance at: https://github.com/bbdjj/CGCOD.