  Due to the high annotation cost of large-scale facial landmark detection
tasks in videos, a semi-supervised paradigm that uses self-training for mining
high-quality pseudo-labels to participate in training has been proposed by
researchers. However, self-training based methods often train with a gradually
increasing number of samples, whose performances vary a lot depending on the
number 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 same
structure. The radical student uses multi-source supervision signals from the
same task to update parameters, while the calm teacher uses a single-source
supervision signal to update parameters. In order to reasonably absorb
student's suggestions, teacher's parameters are updated again through recursive
average filtering. The experimental results prove that asynchronous-learning
framework can effectively filter noise in multi-source supervision signals,
thereby mining the pseudo-labels which are more significant for network
parameter updating. And extensive experiments on 300W, AFLW, and 300VW
benchmarks show that the TSAL framework achieves state-of-the-art performance.
