Fine-grained action recognition typically faces challenges with lower inter-class variances and higher intra-class variances. Supervised contrastive learning is inherently suitable for this task, as it can decrease intra-class feature distances while increasing inter-class ones. However, directly applying it into fine-grained action recognition encounters two main problems. The first problem stems from the heavy training cost associated with supervised contrastive learning, which requires numerous training epochs, each involving double augmentation views per instance. To address this issue, we propose the late-stage supervised contrastive learning (late-SC) strategy, which effectively reduces the number of training epochs needed for the contrastive learning process. The second problem is that supervised contrastive loss does not explicitly consider the semantic distances between fine-grained actions when adjusting …
本文针对细粒度动作识别中类间差异小、类内差异大的挑战，提出了一种语义感知的后期监督对比学习方法。该方法通过后期监督对比学习策略，有效减少了对比学习所需的训练轮次，降低了计算成本；同时引入语义距离建模，在调整特征表示时显式考虑细粒度动作之间的语义关系，从而提升判别能力。实验表明该方法在多个细粒度动作识别基准上取得了优越性能。