Domain-Adaptive Pretraining for Action Recognition in the Dark Scenes

The domain gap between dark scenes and the data used by traditional pretrained models leads to suboptimal performance with the conventional pretrain-finetune approach, and pretraining from scratch is costly. To address this issue, a domain-adaptive pretraining method is proposed to improve action recognition performance in the dark environments. The method integrates an external vision enhancement model for de darkening to introduce critical knowledge for dark scene processing. It also employs a cross-domain self distillation framework to reduce the domain gap of visual representations between illuminated and dark scenes. Through extensive experiments in various dark environment action recognition settings, the proposed approach can achieve a Top1 accuracy of 97.19% on the dark dataset of fully supervised action recognition. In the source-free domain adaptation on the Daily-DA dataset, the accuracy can be improved to 49.11%. In the multi source domain adaptation scenario on the Daily-DA dataset, the Top1 accuracy can reach 54.63%.