  Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers
labeled source knowledge to an unlabeled target domain by relying on accessing
both the source and target data. However, the access to source data is often
restricted or infeasible in real-world scenarios. Under the source data
restrictive circumstances, UDA is less practical. To address this, recent works
have explored solutions under the Source-Free Domain Adaptation (SFDA) setup,
which aims to adapt a source-trained model to the target domain without
accessing source data. Still, existing SFDA approaches use only image-level
information for adaptation, making them sub-optimal in video applications. This
paper studies SFDA for Video Semantic Segmentation (VSS), where temporal
information is leveraged to address video adaptation. Specifically, we propose
Spatio-Temporal Pixel-Level (STPL) contrastive learning, a novel method that
takes full advantage of spatio-temporal information to tackle the absence of
source data better. STPL explicitly learns semantic correlations among pixels
in the spatio-temporal space, providing strong self-supervision for adaptation
to the unlabeled target domain. Extensive experiments show that STPL achieves
state-of-the-art performance on VSS benchmarks compared to current UDA and SFDA
approaches. Code is available at: https://github.com/shaoyuanlo/STPL
