PreyNet: Preying on Camouflaged Objects

Abstract

Species often adopt various camouflage strategies to be seamlessly blended into the surroundings for self-protection. To figure out the concealment, predators have evolved excellent hunting skills. Exploring the intrinsic mechanisms of the predation behavior can offer more insightful glimpse into the task of camouflaged object detection (COD). In this work, we strive to seek answers for accurate COD and propose a PreyNet, which mimics the two processes of predation, namely, initial detection (sensory mechanism) and predator learning (cognitive mechanism). To exploit the sensory process, a bidirectional bridging interaction module (BBIM) is designed for selecting and aggregating initial features in an attentive manner. The predator learning process is formulated as a policy-and-calibration paradigm, with the goal of deciding on uncertain regions and encouraging targeted feature calibration. Besides, we obtain adaptive weight for multi-layer supervision during training via computing on the uncertainty estimation. Extensive experiments demonstrate that our model produces state-of-the-art results on several benchmarks. We further verify the scalability of the predator learning paradigm through applications on top-ranking salient object detection models. Our code is publicly available at \urlhttps://github.com/OIPLab-DUT/PreyNet.