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Due to the high annotation cost of large-scale facial landmark detectiontasks in videos, a semi-supervised paradigm that uses self-training for mininghigh-quality pseudo-labels to participate in training has been proposed byresearchers. However, self-training based methods often train with a graduallyincreasing number of samples, whose performances vary a lot depending on thenumber of pseudo-labeled samples added.  In this paper, we propose a teacher-student asynchronous learning~(TSAL)framework based on the multi-source supervision signal consistency criterion,which implicitly mines pseudo-labels through consistency constraints.Specifically, the TSAL framework contains two models with exactly the samestructure. The radical student uses multi-source supervision signals from thesame task to update parameters, while the calm teacher uses a single-sourcesupervision signal to update parameters. In order to reasonably absorbstudent's suggestions, teacher's parameters are updated again through recursiveaverage filtering. The experimental results prove that asynchronous-learningframework can effectively filter noise in multi-source supervision signals,thereby mining the pseudo-labels which are more significant for networkparameter updating. And extensive experiments on 300W, AFLW, and 300VWbenchmarks show that the TSAL framework achieves state-of-the-art performance.

Teacher-Student Asynchronous Learning with Multi-Source Consistency for Facial Landmark Detection