Camouflaged object detection with counterfactual intervention

Abstract

Camouflaged object detection (COD) aims to identify camouflaged objects hiding in their surroundings, which is a valuable yet challenging task. The main challenge is that there are ambiguous semantic biases in the camouflaged object datasets, which affect the results of COD. To address this challenge, we design a counterfactual intervention network (CINet) to mitigate the influences of ambiguous semantic biases and obtain accurate COD. Specifically, our CINet consists of three key modules, i.e., texture-aware interaction module (TIM), context-aware fusion module (CFM), and counterfactual intervention module (CIM). The TIM is designed to extract the refined textures for accurate localization, the CFM is proposed to fuse the multi-scale contextual features to enhance the detection performance, and the CIM is presented to learn more effective textures and make unbiased predictions. Unlike most existing COD methods that directly capture contextual features through the final loss function, we develop a counterfactual intervention strategy to learn more effective contextual textures. Extensive experiments on four challenging benchmark datasets demonstrate that our CINet significantly outperforms 31 state-of-the-art methods.