Boosting Semi-Supervised Facial Attribute Recognition with Dynamic Threshold Pairs

Semi-supervised learning (SSL) has proven effective in assigning a pseudo-label to a confident sample whose largest class probability is above a fixed threshold. However, in the context of semi-supervised facial attribute recognition (SSFAR), where a sample is associated with multiple presence and absence pseudo-labels, directly applying existing SSL methods is challenging due to two issues: 1) the lack of a clear boundary between presence and absence predictions for an attribute makes it difficult to distinguish them using a single threshold; 2) the learning difficulty varies across attributes, so the fixed strategy fails to adaptively learn different attributes. To address these challenges, we propose Dynamic thrEShold Pairs (DESP), a simple yet effective method to handle the SSFAR problem. Specifically, during each training stage, we derive two sets for each attribute from labeled samples, which contain the predicted probabilities of presence and absence, respectively. We then compute the mid-ranges of the two sets as paired presence and absence thresholds. Finally, we assign a presence or absence pseudo-label for the attribute to an unlabeled sample when its prediction exceeds the presence threshold or falls below the absence threshold. Extensive experiments on the CelebA and LFWA datasets demonstrate that DESP achieves superior performance compared to state-of-the-art methods, especially in the case of scarce labeled samples. Also, DESP performs well on multi-label datasets such as Pascal VOC and MS-COCO.