  With the recent surge in the research of vision transformers, they have
demonstrated remarkable potential for various challenging computer vision
applications, such as image recognition, point cloud classification as well as
video understanding. In this paper, we present empirical results for training a
stronger video vision transformer on the EPIC-KITCHENS-100 Action Recognition
dataset. Specifically, we explore training techniques for video vision
transformers, such as augmentations, resolutions as well as initialization,
etc. With our training recipe, a single ViViT model achieves the performance of
47.4\% on the validation set of EPIC-KITCHENS-100 dataset, outperforming what
is reported in the original paper by 3.4%. We found that video transformers are
especially good at predicting the noun in the verb-noun action prediction task.
This makes the overall action prediction accuracy of video transformers notably
higher than convolutional ones. Surprisingly, even the best video transformers
underperform the convolutional networks on the verb prediction. Therefore, we
combine the video vision transformers and some of the convolutional video
networks and present our solution to the EPIC-KITCHENS-100 Action Recognition
competition.
