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.

翻译：缓解噪声伪标签仍然是半监督时序动作定位（SS-TAL）中的一个关键挑战。现有方法通常基于严格条件过滤伪标签，但它们往往分别评估分类质量和定位质量，导致伪标签排序与选择欠佳。具体而言，在选定的正样本中可能存在不准确的伪标签，同时可靠的样本可能被错误地分配为负样本。为解决这些问题，我们提出了一种新颖的自适应伪标签学习（APL）框架，以促进更好的伪标签选择。具体来说，为提高排序质量，我们提出了自适应标签质量评估（ALQA），以联合学习分类置信度与定位可靠性，并随后基于联合得分动态选择伪标签。此外，我们提出了一种实例级一致性判别器（ICD），基于实例间内在一致性，同时消除模糊正样本并挖掘潜在正样本，从而实现更精确的选择。我们进一步引入了一种通用的无监督动作感知对比预训练（ACP），以增强动作内部以及动作与背景之间的区分度，这有益于SS-TAL。在THUMOS14和ActivityNet v1.3上进行的大量实验表明，我们的方法在各种半监督设置下均取得了最先进的性能。