  While action recognition (AR) has gained large improvements with the
introduction of large-scale video datasets and the development of deep neural
networks, AR models robust to challenging environments in real-world scenarios
are still under-explored. We focus on the task of action recognition in dark
environments, which can be applied to fields such as surveillance and
autonomous driving at night. Intuitively, current deep networks along with
visual enhancement techniques should be able to handle AR in dark environments,
however, it is observed that this is not always the case in practice. To dive
deeper into exploring solutions for AR in dark environments, we launched the
UG2+ Challenge Track 2 (UG2-2) in IEEE CVPR 2021, with a goal of evaluating and
advancing the robustness of AR models in dark environments. The challenge
builds and expands on top of a novel ARID dataset, the first dataset for the
task of dark video AR, and guides models to tackle such a task in both fully
and semi-supervised manners. Baseline results utilizing current AR models and
enhancement methods are reported, justifying the challenging nature of this
task with substantial room for improvements. Thanks to the active participation
from the research community, notable advances have been made in participants'
solutions, while analysis of these solutions helped better identify possible
directions to tackle the challenge of AR in dark environments.
