反思半监督地标检测的自我训练:无选择方法

Haibo Jin;Haoxuan Che;Hao Chen
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引用次数: 0

摘要

自我训练是一种简单而有效的半监督学习方法,其中伪标签选择在处理确认偏差方面发挥着重要作用。尽管这种方法很受欢迎,但将自我训练应用于地标检测却面临三个问题:1)选择的置信伪标签往往包含数据偏差,这可能会损害模型性能;2)由于定位任务可能对噪声伪标签很敏感,因此决定一个合适的样本选择阈值并不容易;3)坐标回归不输出置信度,这使得基于选择的自我训练变得不可行。为了解决上述问题,我们提出了地标检测自我训练(STLD)方法,这种方法不需要明确的伪标签选择。取而代之的是,STLD 构建了一个处理确认偏差的任务课程,在一轮又一轮的自我训练中,从信心较高的任务逐步过渡到信心较低的任务。伪预训练和收缩回归是这种课程的两个基本组成部分,前者是课程的第一个任务,用于提供更好的模型初始化,后者则在后几轮任务中进一步添加,以从粗到细的方式直接利用伪标签。在三个面部检测基准和一个医疗地标检测基准上的实验表明,STLD 在半监督和全监督环境下的表现始终优于现有方法。代码见 https://github.com/jhb86253817/STLD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rethinking Self-Training for Semi-Supervised Landmark Detection: A Selection-Free Approach
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. The code is available at https://github.com/jhb86253817/STLD .
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