  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. Experimental results on four benchmark semantic
segmentation datasets confirm that the proposed method consistently outperforms
existing methods across various evaluation protocols. In particular, with
DeepLabV3+ and ResNet-101 network settings, PrevMatch outperforms the existing
state-of-the-art method, Diverse Co-training, by +1.6 mIoU on Pascal VOC with
only 92 annotated images, while achieving 2.4 times faster training.
Furthermore, the results indicate that PrevMatch induces stable optimization,
particularly in benefiting classes that exhibit poor performance. Code is
available at https://github.com/wooseok-shin/PrevMatch
