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Recently, attempts have been made to collect millions of videos to train CNNmodels for action recognition in videos. However, curating such large-scalevideo datasets requires immense human labor, and training CNNs on millions ofvideos demands huge computational resources. In contrast, collecting actionimages from the Web is much easier and training on images requires much lesscomputation. In addition, labeled web images tend to contain discriminativeaction poses, which highlight discriminative portions of a video's temporalprogression. We explore the question of whether we can utilize web actionimages to train better CNN models for action recognition in videos. We collect23.8K manually filtered images from the Web that depict the 101 actions in theUCF101 action video dataset. We show that by utilizing web action images alongwith videos in training, significant performance boosts of CNN models can beachieved. We then investigate the scalability of the process by leveragingcrawled web images (unfiltered) for UCF101 and ActivityNet. We replace 16.2Mvideo frames by 393K unfiltered images and get comparable performance.

Do Less and Achieve More: Training CNNs for Action Recognition Utilizing Action Images from the Web