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We address the problem of fine-grained action localization from temporallyuntrimmed web videos. We assume that only weak video-level annotations areavailable for training. The goal is to use these weak labels to identifytemporal segments corresponding to the actions, and learn models thatgeneralize to unconstrained web videos. We find that web images queried byaction names serve as well-localized highlights for many actions, but arenoisily labeled. To solve this problem, we propose a simple yet effectivemethod that takes weak video labels and noisy image labels as input, andgenerates localized action frames as output. This is achieved by cross-domaintransfer between video frames and web images, using pre-trained deepconvolutional neural networks. We then use the localized action frames to trainaction recognition models with long short-term memory networks. We collect afine-grained sports action data set FGA-240 of more than 130,000 YouTubevideos. It has 240 fine-grained actions under 85 sports activities. Convincingresults are shown on the FGA-240 data set, as well as the THUMOS 2014localization data set with untrimmed training videos.

Temporal Localization of Fine-Grained Actions in Videos by Domain Transfer from Web Images