  This paper presents our solution to the AVA-Kinetics Crossover Challenge of
ActivityNet workshop at CVPR 2021. Our solution utilizes multiple types of
relation modeling methods for spatio-temporal action detection and adopts a
training strategy to integrate multiple relation modeling in end-to-end
training over the two large-scale video datasets. Learning with memory bank and
finetuning for long-tailed distribution are also investigated to further
improve the performance. In this paper, we detail the implementations of our
solution and provide experiments results and corresponding discussions. We
finally achieve 40.67 mAP on the test set of AVA-Kinetics.
