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

22 Dec 2015  ·  Shugao Ma, Sarah Adel Bargal, Jianming Zhang, Leonid Sigal, Stan Sclaroff ·

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.

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Results from the Paper


Ranked #11 on Action Recognition on ActivityNet (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Action Recognition ActivityNet VGG19 + 393K webcam images mAP 53.8 # 11
Action Recognition ActivityNet VGG19 mAP 52.3 # 13

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