  Recognizing human actions in adverse lighting conditions presents significant
challenges in computer vision, with wide-ranging applications in visual
surveillance and nighttime driving. Existing methods tackle action recognition
and dark enhancement separately, limiting the potential for end-to-end learning
of spatiotemporal representations for video action classification. This paper
introduces Dark Transformer, a novel video transformer-based approach for
action recognition in low-light environments. Dark Transformer leverages
spatiotemporal self-attention mechanisms in cross-domain settings to enhance
cross-domain action recognition. By extending video transformers to learn
cross-domain knowledge, Dark Transformer achieves state-of-the-art performance
on benchmark action recognition datasets, including InFAR, XD145, and ARID. The
proposed approach demonstrates significant promise in addressing the challenges
of action recognition in adverse lighting conditions, offering practical
implications for real-world applications.
