关键词: 动作识别, 半监督学习（SSL）, 视觉Transformer, logit标准化预处理, 负学习

Abstract: Action recognition, as a pattern recognition technique, aims to identify and classify human actions or behaviors by analyzing video or image sequences. Given the exponential increase in video data, semi-supervised learning has been incorporated into action recognition models; however, there is still significant room for improvement in classification performance. The vision Transformer has demonstrated superior performance compared to CNN in image processing, thereby enhancing the training paradigm of video Transformers in semi-supervised learning. Firstly, pre-trained weights are employed to initialize the network, addressing the high training cost associated with Transformer architectures. Secondly, logit standardization preprocessing is introduced to remove the forced matching constraint between student and teacher logits. Finally, negative learning techniques are integrated to dynamically assess model performance and allocate negative pseudo-labels, addressing the issue of inadequate utilization of ambiguous prediction examples. The experimental results demonstrate that the improved semi-supervised video Transformer network achieves superior recognition performance compared to traditional convolutional networks on two widely used video action recognition datasets, UCF-101 and HMDB-51. Specifically, the improved network model outperforms the baseline model on the UCF-101 dataset by 6.4?and 1.5 percentage points at 1% and 10% label rates, respectively. On the HMDB-51 dataset, the improved model shows improvements of 5.2, 3.6, and 3.1 percentage points at 40%, 50%, and 60% label rates, respectively.

Key words: action recognition, semi-supervised learning（SSL）, vision Transformer, logit standardization preprocessing, negative learning