TPRNet: camouflaged object detection via transformer-induced progressive refinement network

Camouflaged object detection (COD) is a challenging task which aims to detect objects similar to the surrounding environment. In this paper, we propose a transformer-induced progressive refinement network (TPRNet) to solve challenging COD tasks. Specifically, our network includes a Transformer-induced Progressive Refinement Module (TPRM) and a Semantic-Spatial Interaction Enhancement Module (SIEM). In TPRM, high-level features with rich semantic information are integrated through transformers as prior guidance, and then, it is sent to the refinement concurrency unit (RCU), and the accurately positioned feature area is obtained through a progressive refinement strategy. In SIEM, we perform feature interaction to localizedaccurate semantic features and low-level features to obtain rich fine-grained clues and increase the symbolic power of boundary features. Extensive experiments on four widely used benchmark datasets (i.e., CAMO, CHAMELEON, COD10K, and NC4K) demonstrate that our TPRNet is an effective COD model and outperforms state-of-the-art models. The code is available https://github.com/zhangyiao970914/TPRNet.