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Recent temporal action segmentation approaches need frame annotations duringtraining to be effective. These annotations are very expensive andtime-consuming to obtain. This limits their performances when only limitedannotated data is available. In contrast, we can easily collect a large corpusof in-domain unannotated videos by scavenging through the internet. Thus, thispaper proposes an approach for the temporal action segmentation task that cansimultaneously leverage knowledge from annotated and unannotated videosequences. Our approach uses multi-stream distillation that repeatedly refinesand finally combines their frame predictions. Our model also predicts theaction order, which is later used as a temporal constraint while estimatingframes labels to counter the lack of supervision for unannotated videos. In theend, our evaluation of the proposed approach on two different datasetsdemonstrates its capability to achieve comparable performance to the fullsupervision despite limited annotation.

Distill and Collect for Semi-Supervised Temporal Action Segmentation