An attention-guided and prior-embedded approach with multi-task learning for shadow detection

Abstract Shadow detection is a fundamental and challenging task, requiring understanding accurately the visual semantic context of the shadow region and backgrounds. In this paper, we propose an attention-guided and prior-embedded approach with multi-task learning for shadow detection task. Different from most existing works, we introduce the effective multi-task learning into this target detection task to add the high-level prior into the detection process, instead of using the pertained weighting network as the front-end module and complex recurrent network. Especially, we also employ a channel attention-guided module to complement the high-level feature and low-level feature. Moreover, for the proposed approach with multi-task learning, we design the weighted loss function for effective training. Experimental results on two public available benchmarks demonstrate our approach achieves competitive results than the existing typical shadow detection approaches.