Video Domain Adaptation

Video domain adaptation focuses on training video analysis models that generalize well across different video datasets, overcoming the limitations of models trained on a single, often limited, source domain. Current research emphasizes unsupervised methods, leveraging techniques like contrastive learning, self-supervised pre-training, and transformer architectures (including Vision Transformers) to align features between source and target domains, often incorporating temporal information effectively. This field is crucial for improving the robustness and real-world applicability of video understanding systems, reducing the reliance on extensive, costly annotation of diverse video data.