Don't worry about noisy labels in soft shadow detection

Soft shadow is harder to detect than hard shadow as its complex characteristics (i.e., low-contrast, irregular shape, and ambiguous shadow boundaries). To improve the detecting capacity of these images, in this paper, we create a new benchmark for soft shadow detection and then design a reasonable supervision strategy to alleviate the effect of annotation noises. Next, we present a general shadow detection framework based on transformer to deal with complex scenes. Concretely, we combine the traditional channel attention and recent popular self-attention into our network. Moreover, we introduce a deep supervision mechanism that performs deep layer supervision to "guide " early classification results at each layer, which can further improve our detection performance. Finally, experimental results on three datasets show that our shadow transformer can be favorable against current state-of-the-art detectors.