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Image matting methods based on deep learning have made tremendous success. However, the success of previous image matting methods typically relies on a massive amount of pixel-level labeled data, which are time-consuming and costly to obtain. This paper first proposes a semi-supervised deep learning matting algorithm based on semantic consistency of trimaps (Tri-SSL), which uses trimaps to provide weakly supervised signals for the unlabeled data, to reduce the labeling cost. Tri-SSL is a single-stage semi-supervised algorithm that consists of a supervised branch and a weakly supervised branch that share the same network in one iteration during training. The supervised branch is consistent with standard supervised matting methods. In the weakly supervised branch, trimaps of different granularities are used as weakly supervised signals for unlabeled images, and the two trimaps are naturally perturbed samples. Orientation consistency constraints are imposed on the prediction results of trimaps of different granuliarty and the intermediate features of the network. Experimental results show that Tri-SSL improves model performance by effectively utilizing unlabeled data.

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