In semi-supervised semantic segmentation, the Mean Teacher- and co-training-based approaches are employed to mitigate confirmation bias and coupling problems. However, despite their high performance, these approaches frequently involve complex training pipelines and a substantial computational burden, limiting the scalability and compatibility of these methods. In this paper, we propose a PrevMatch framework that effectively mitigates the aforementioned limitations by maximizing the utilization of the temporal knowledge obtained during the training process. The PrevMatch framework relies on two core strategies: (1) we reconsider the use of temporal knowledge and thus directly utilize previous models obtained during training to generate additional pseudo-label guidance, referred to as previous guidance. (2) we design a highly randomized ensemble strategy to maximize the effectiveness of the previous guidance. PrevMatch, a simple yet effective plug-in method, can be seamlessly integrated into existing semi-supervised learning frameworks with minimal computational overhead. Experimental results on three benchmark semantic segmentation datasets show that incorporating PrevMatch into existing methods significantly improves their performance. Furthermore, our analysis indicates that PrevMatch facilitates stable optimization during training, resulting in improved generalization performance.

翻译：在半监督语义分割中，基于Mean Teacher和协同训练的方法被用于缓解确认偏差和耦合问题。然而，尽管这些方法性能优异，它们通常涉及复杂的训练流程和巨大的计算负担，限制了方法的可扩展性和兼容性。本文提出PrevMatch框架，通过最大化利用训练过程中获得的时序知识，有效缓解上述限制。PrevMatch框架依赖于两个核心策略：（1）我们重新审视时序知识的运用，从而直接利用训练过程中获得的先前模型来生成额外的伪标签指导，称为先前指导。（2）我们设计了一种高度随机化的集成策略，以最大化先前指导的有效性。PrevMatch作为一种简单而有效的即插即用方法，能够以极小的计算开销无缝集成到现有的半监督学习框架中。在三个基准语义分割数据集上的实验结果表明，将PrevMatch融入现有方法能显著提升其性能。此外，我们的分析表明，PrevMatch促进了训练过程中的稳定优化，从而提升了泛化性能。