Semi-supervised Video Shadow Detection via Image-assisted Pseudo-label Generation

Zipei Chen, Xiao Lu, Ling Zhang, Chunxia Xiao

Abstract: Although learning-based methods have shown their potential for image shadow detection, video shadow detection is still a challenging problem. It is due to the absence of large-scale, temporally consistent annotated video shadow detection dataset. To this end, we propose a semi-supervised video shadow detection method by seeking the assistance of the existing labeled image dataset to generate pseudo-labels as the additional supervision signals. Specifically, we first introduce a novel image-assisted video pseudo-label generator with a spatio-temporally aligned network (STANet). It generates high-quality and temporally consistent pseudo-labels. Then, with these pseudo-labels, we propose an uncertainty-guided semi-supervised learning strategy to reduce the impact of noise from them. Moreover, we also design a memory propagated long-term network (MPLNet), which produces video shadow detection results with long-term consistency in a light-weight way by using the memory mechanism. Extensive experiments on ViSha and our collected real-world video shadow detection dataset RVSD show that our approach not only achieves superior performance in the benchmark dataset but also generalizes well in more practical applications, which demonstrates the effectiveness of our method.