  We present an approach to perform supervised action recognition in the dark.
In this work, we present our results on the ARID dataset. Most previous works
only evaluate performance on large, well illuminated datasets like Kinetics and
HMDB51. We demonstrate that our work is able to achieve a very low error rate
while being trained on a much smaller dataset of dark videos. We also explore a
variety of training and inference strategies including domain transfer
methodologies and also propose a simple but useful frame selection strategy.
Our empirical results demonstrate that we beat previously published baseline
models by 11%.
