Abstract
Semi-Supervised Learning (SSL) has shown tremendous potential to improve the predictive performance of deep learning models when annotations are hard to obtain. However, the application of SSL has so far been mainly studied in the context of image classification. In this work, we present a semi-supervised approach for spatial-temporal action localization. We introduce a dual guidance network to select better pseudo-bounding boxes. It combines a frame-level classification with a bounding-box prediction to enforce action class consistency across frames and boxes. Our evaluation across well-known spatial-temporal action localization datasets, namely UCF101-24 , J-HMDB-21 and AVA shows that the proposed module considerably enhances the model's performance in limited labeled data settings. Our framework achieves superior results compared to extended image-based semi-supervised baselines.
Abstract (translated)
半监督学习（Semi-Supervised Learning，SSL）在标注难以获取的情况下，展示了显著提升深度学习模型预测性能的巨大潜力。然而，到目前为止，SSL的应用主要是在图像分类的背景下进行研究。在这项工作中，我们提出了一种用于空间-时间动作定位的半监督方法。我们引入了一个双引导网络来选择更好的伪边界框（pseudo-bounding boxes）。该网络结合了帧级分类和边界框预测，以确保不同帧和框之间的动作类别一致性。我们在著名的空间-时间动作定位数据集UCF101-24、J-HMDB-21和AVA上的评估表明，所提出的模块在有限标注数据的情况下显著提升了模型的性能。我们的框架相比于扩展的基于图像的半监督基准方法取得了更好的结果。
URL
https://arxiv.org/abs/2507.21247
