CVPR 2023 - Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free

In this episode we discuss Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free by Authors: - Shao-Yuan Lo - Poojan Oza - Sumanth Chennupati - Alejandro Galindo - Vishal M. Patel Affiliations: - Shao-Yuan Lo: Johns Hopkins University - Poojan Oza: Amazon - Sumanth Chennupati: Amazon - Alejandro Galindo: Amazon - Vishal M. Patel: Johns Hopkins University. The paper discusses unsupervised domain adaptation (UDA) of semantic segmentation, which transfers labeled source knowledge to an unlabeled target domain by accessing both the source and target data. However, access to the source data is often restricted or infeasible, making UDA less practical. To address this, recent works have explored Source-Free Domain Adaptation (SFDA), but current SFDA approaches use only image-level information, which is sub-optimal for video applications. The paper proposes a Spatio-Temporal Pixel-Level (STPL) contrastive learning method that uses spatio-temporal information to tackle the absence of source data better, achieving state-of-the-art performance on Video Semantic Segmentation (VSS) benchmarks compared to existing UDA and SFDA approaches.