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We address the problem of temporal action localization in videos. We poseaction localization as a structured prediction over arbitrary-length temporalwindows, where each window is scored as the sum of frame-wise classificationscores. Additionally, our model classifies the start, middle, and end of eachaction as separate components, allowing our system to explicitly model eachaction's temporal evolution and take advantage of informative temporaldependencies present in this structure. In this framework, we localize actionsby searching for the structured maximal sum, a problem for which we develop anovel, provably-efficient algorithmic solution. The frame-wise classificationscores are computed using features from a deep Convolutional Neural Network(CNN), which are trained end-to-end to directly optimize for a novel structuredobjective. We evaluate our system on the THUMOS 14 action detection benchmarkand achieve competitive performance.

Temporal Action Localization by Structured Maximal Sums