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.

阴影检测是一项基本且具有挑战性的任务，需要准确了解阴影区域和背景的视觉语义环境。在本文中，我们提出了一种针对多目标学习的注意力引导和嵌入式方法，用于阴影检测任务。与大多数现有工作不同，我们将有效的多任务学习引入到目标检测任务中，以在检测过程中添加高级先验，而不是使用相关的加权网络作为前端模块和复杂的递归网络。特别是，我们还采用了频道注意引导模块来补充高级功能和低级功能。此外，对于所提出的多任务学习方法，我们设计了加权损失函数以进行有效的训练。