  Alleviating noisy pseudo labels remains a key challenge in Semi-Supervised
Temporal Action Localization (SS-TAL). Existing methods often filter pseudo
labels based on strict conditions, but they typically assess classification and
localization quality separately, leading to suboptimal pseudo-label ranking and
selection. In particular, there might be inaccurate pseudo labels within
selected positives, alongside reliable counterparts erroneously assigned to
negatives. To tackle these problems, we propose a novel Adaptive Pseudo-label
Learning (APL) framework to facilitate better pseudo-label selection.
Specifically, to improve the ranking quality, Adaptive Label Quality Assessment
(ALQA) is proposed to jointly learn classification confidence and localization
reliability, followed by dynamically selecting pseudo labels based on the joint
score. Additionally, we propose an Instance-level Consistency Discriminator
(ICD) for eliminating ambiguous positives and mining potential positives
simultaneously based on inter-instance intrinsic consistency, thereby leading
to a more precise selection. We further introduce a general unsupervised
Action-aware Contrastive Pre-training (ACP) to enhance the discrimination both
within actions and between actions and backgrounds, which benefits SS-TAL.
Extensive experiments on THUMOS14 and ActivityNet v1.3 demonstrate that our
method achieves state-of-the-art performance under various semi-supervised
settings.
