While semi-supervised learning (SSL) has yielded promising results, the more realistic SSL scenario remains to be explored, in which the unlabeled data exhibits extremely high recognition difficulty, e.g., fine-grained visual classification in the context of SSL (SS-FGVC). The increased recognition difficulty on fine-grained unlabeled data spells disaster for pseudo-labeling accuracy, resulting in poor performance of the SSL model. To tackle this challenge, we propose Soft Label Selection with Confidence-Aware Clustering based on Class Transition Tracking (SoC) by reconstructing the pseudo-label selection process by jointly optimizing Expansion Objective and Shrinkage Objective, which is based on a soft label manner. Respectively, the former objective encourages soft labels to absorb more candidate classes to ensure the attendance of ground-truth class, while the latter encourages soft labels to reject more noisy classes, which is theoretically proved to be equivalent to entropy minimization. In comparisons with various state-of-the-art methods, our approach demonstrates its superior performance in SS-FGVC. Checkpoints and source code are available at https://github.com/NJUyued/SoC4SS-FGVC.

翻译：摘要：尽管半监督学习已取得显著成果，但更贴近实际的半监督场景仍有待探索——其中未标注数据具有极高的识别难度，例如半监督学习框架下的细粒度视觉分类（SS-FGVC）。未标注细粒度数据识别难度的增加对伪标签准确性造成灾难性影响，导致半监督模型性能低下。为应对这一挑战，我们提出基于类别转移追踪的置信度感知聚类软标签选择方法（SoC），通过联合优化扩展目标与收缩目标重构伪标签选择过程，该方法基于软标签机制。具体而言，前者鼓励软标签吸收更多候选类别以确保真实类别被覆盖，后者则促使软标签拒绝更多噪声类别，理论上可证明其等价于熵最小化。与多种前沿方法的对比实验表明，本方法在SS-FGVC任务中展现出卓越性能。模型权重与源代码已开源在 https://github.com/NJUyued/SoC4SS-FGVC。