Rethinking Self-training for Semi-supervised Landmark Detection: A
  Selection-free Approach
Haibo Jin Haoxuan Che Hao Chen
Self-training is a simple yet effective method for semi-supervised learning,
during which pseudo-label selection plays an important role for handling
confirmation bias. Despite its popularity, applying self-training to landmark
detection faces three problems: 1) The selected confident pseudo-labels often
contain data bias, which may hurt model performance; 2) It is not easy to
decide a proper threshold for sample selection as the localization task can be
sensitive to noisy pseudo-labels; 3) coordinate regression does not output
confidence, making selection-based self-training infeasible. To address the
above issues, we propose Self-Training for Landmark Detection (STLD), a method
that does not require explicit pseudo-label selection. Instead, STLD constructs
a task curriculum to deal with confirmation bias, which progressively
transitions from more confident to less confident tasks over the rounds of
self-training. Pseudo pretraining and shrink regression are two essential
components for such a curriculum, where the former is the first task of the
curriculum for providing a better model initialization and the latter is
further added in the later rounds to directly leverage the pseudo-labels in a
coarse-to-fine manner. Experiments on three facial and one medical landmark
detection benchmark show that STLD outperforms the existing methods
consistently in both semi- and omni-supervised settings.