  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.
