DCQNet: Collaborative Camouflaged Object Detection Using Cross-Sample and Cross-Scale Network

Camouflaged object detection (COD) aims to identify objects that blend into the surrounding backgrounds, which has been a hot topic in recent years, with many different optimization strategies being explored. Among these, collaborative detection, a recently proposed solution for COD, has shown outstanding performance. However, current collaborative detection methods adopt a "one-to-many" pattern, failing to fully leverage the advantages of collaborative detection. Moreover, existing approaches to disguised target detection overlook the importance of high-level features. To address these issues, we propose a novel dualcross query network (DCQNet). It effectively capitalizes on the commonalities among objects and the directive role of high-level features in enhancing low-level features. Specifically, we designed a cross-sample query module and a cross-scale query module to collaboratively locate the object and guide low-level features, respectively. Extensive experimental results demonstrate that DCQNet outperforms state-of-the-art (SOTA) methods on the CoCOD8K dataset.