# Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
## 6 Conclusions  
The data labeling cost of matting tasks is high, and current deep matting methods are all trained using datasets which are enlarged by combining one foreground with several background images to meet the training requirements of the deep learning models. To reduce the labeling cost, this paper first proposes a semi-supervised matting algorithm Tri-SSL based on the semantic consistency of trimaps. In the matting task, a finely labeled trimap can produce a better alpha matte than a coarse trimap, and the different-granularity trimaps have inherent semantic consistency correlation. Based on this rule, this paper uses trimaps as weak supervision signals for the unlabeled images. Two trimaps with different granularity are made for the unlabeled data, passing through the network to obtain the  
Figure 6: The results of Tri-SSL working on real-world images. alpha prediction separately. The directional consistency constraints are imposed on the prediction results of different-granularity trimaps and the intermediate features of the network. Experimental results show that Tri-SSL can effectively use the weak supervision information of trimaps to improve the model's performance, achieving new state-of-the-art performance. Conceptualization, J.L. and Y.K. ; methodology, Y.K. and J.L. ; software, Y.K. ; validation, Y.K. ; formal analysis, Y.K. ; investigation, Y.K. ; resources, X.L. ; data curation, Y.K. ; writing--original draft preparation, Y.K. ; writing--review and editing, Y.K., L.H., J.L. and X.L. ; visualization, Y.K. ; supervision, X.L. ; project administration, X.L. ; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript. The Science and Technology Innovation Plan of Shanghai Science and Technology Commission, under Grant 21511100600. Data Availability Statement: Data sharing not applicable. The authors declare no conflict of interest. * [PERSON] et al. (2023) [PERSON]; [PERSON]; [PERSON]; [PERSON] Effective Local-Global Transformer for Natural Image Matting. _IEEE Trans. Circuits Syst. Video Technol._**2023**. [CrossRef]
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