Abstract
The purpose of this study is to determine whether current
video datasets have sufficient data for training very deep
convolutional neural networks (CNNs) with spatio-temporal
three-dimensional (3D) kernels. Recently, the performance
levels of 3D CNNs in the field of action recognition have
improved significantly. However, to date, conventional research has only explored relatively shallow 3D architectures.
We examine the architectures of various 3D CNNs from relatively shallow to very deep ones on current video datasets.
Based on the results of those experiments, the following conclusions could be obtained: (i) ResNet-18 training resulted
in significant overfitting for UCF-101, HMDB-51, and ActivityNet but not for Kinetics. (ii) The Kinetics dataset has
sufficient data for training of deep 3D CNNs, and enables
training of up to 152 ResNets layers, interestingly similar
to 2D ResNets on ImageNet. ResNeXt-101 achieved 78.4%
average accuracy on the Kinetics test set. (iii) Kinetics pretrained simple 3D architectures outperforms complex 2D architectures, and the pretrained ResNeXt-101 achieved 94.5%
and 70.2% on UCF-101 and HMDB-51, respectively.
The use of 2D CNNs trained on ImageNet has produced
significant progress in various tasks in image. We believe
that using deep 3D CNNs together with Kinetics will retrace
the successful history of 2D CNNs and ImageNet, and stimulate advances in computer vision for videos. The codes and
pretrained models used in this study are publicly available