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.

自我训练（self-training）方法在处理确认偏倚问题上具有重要作用，但在地标检测领域应用时面临着自适应选择伪标签、确定样本选择阈值和选择自训练的困难等问题。为了解决这些问题，本文提出了自我训练地标检测（STLD）方法，使用任务学习计划代替显式伪标签选择，从而达到从有信心到无信心任务逐步迁移的目的，并通过伪预训练和收缩回归两个重要组成部分实现，实验证明 STLD 在半监督和全监督设置下均优于现有方法。

半监督地标检测中的自训练再思考：一种无选择的方法

Rethinking Self-training for Semi-supervised Landmark Detection: A  Selection-free Approach

The problem of continual learning has attracted rising attention in recent
years. However, few works have questioned the commonly used learning setup,
based on a task curriculum of random class. This differs significantly from
human continual learning, which is guided by taxonomic curricula. In this work,
we propose the Taxonomic Class Incremental Learning (TCIL) problem. In TCIL,
the task sequence is organized based on a taxonomic class tree. We unify
existing approaches to CIL and taxonomic learning as parameter inheritance
schemes and introduce a new such scheme for the TCIL learning. This enables the
incremental transfer of knowledge from ancestor to descendant class of a class
taxonomy through parameter inheritance. Experiments on CIFAR-100 and
ImageNet-100 show the effectiveness of the proposed TCIL method, which
outperforms existing SOTA methods by 2% in terms of final accuracy on CIFAR-100
and 3% on ImageNet-100.

本文提出了基于分类法系列的 Taxonomic Class Incremental Learning (TCIL) 问题，通过参数继承实现了知识的增量传递，实验证明 TCIL 方法在 CIFAR-100 和 ImageNet-100 上均优于 SOTA 方法。