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Video shadow detection aims to generate consistent shadow predictions amongvideo frames. However, the current approaches suffer from inconsistent shadowpredictions across frames, especially when the illumination and backgroundtextures change in a video. We make an observation that the inconsistentpredictions are caused by the shadow feature inconsistency, i.e., the featuresof the same shadow regions show dissimilar proprieties among the nearbyframes.In this paper, we present a novel Shadow-Consistent Correspondencemethod (SC-Cor) to enhance pixel-wise similarity of the specific shadow regionsacross frames for video shadow detection. Our proposed SC-Cor has three mainadvantages. Firstly, without requiring the dense pixel-to-pixel correspondencelabels, SC-Cor can learn the pixel-wise correspondence across frames in aweakly-supervised manner. Secondly, SC-Cor considers intra-shadow separability,which is robust to the variant textures and illuminations in videos. Finally,SC-Cor is a plug-and-play module that can be easily integrated into existingshadow detectors with no extra computational cost. We further design a newevaluation metric to evaluate the temporal stability of the video shadowdetection results. Experimental results show that SC-Cor outperforms the priorstate-of-the-art method, by 6.51% on IoU and 3.35% on the newly introducedtemporal stability metric.

Learning Shadow Correspondence for Video Shadow Detection