  Tiny Actions Challenge focuses on understanding human activities in
real-world surveillance. Basically, there are two main difficulties for
activity recognition in this scenario. First, human activities are often
recorded at a distance, and appear in a small resolution without much
discriminative clue. Second, these activities are naturally distributed in a
long-tailed way. It is hard to alleviate data bias for such heavy category
imbalance. To tackle these problems, we propose a comprehensive recognition
solution in this paper. First, we train video backbones with data balance, in
order to alleviate overfitting in the challenge benchmark. Second, we design a
dual-resolution distillation framework, which can effectively guide
low-resolution action recognition by super-resolution knowledge. Finally, we
apply model en-semble with post-processing, which can further boost
per-formance on the long-tailed categories. Our solution ranks Top-1 on the
leaderboard.
