  Recently, attempts have been made to collect millions of videos to train CNN
models for action recognition in videos. However, curating such large-scale
video datasets requires immense human labor, and training CNNs on millions of
videos demands huge computational resources. In contrast, collecting action
images from the Web is much easier and training on images requires much less
computation. In addition, labeled web images tend to contain discriminative
action poses, which highlight discriminative portions of a video's temporal
progression. We explore the question of whether we can utilize web action
images to train better CNN models for action recognition in videos. We collect
23.8K manually filtered images from the Web that depict the 101 actions in the
UCF101 action video dataset. We show that by utilizing web action images along
with videos in training, significant performance boosts of CNN models can be
achieved. We then investigate the scalability of the process by leveraging
crawled web images (unfiltered) for UCF101 and ActivityNet. We replace 16.2M
video frames by 393K unfiltered images and get comparable performance.
