  A recent work from Bello shows that training and scaling strategies may be
more significant than model architectures for visual recognition. This short
note studies effective training and scaling strategies for video recognition
models. We propose a simple scaling strategy for 3D ResNets, in combination
with improved training strategies and minor architectural changes. The
resulting models, termed 3D ResNet-RS, attain competitive performance of 81.0
on Kinetics-400 and 83.8 on Kinetics-600 without pre-training. When pre-trained
on a large Web Video Text dataset, our best model achieves 83.5 and 84.3 on
Kinetics-400 and Kinetics-600. The proposed scaling rule is further evaluated
in a self-supervised setup using contrastive learning, demonstrating improved
performance. Code is available at:
https://github.com/tensorflow/models/tree/master/official.
