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While many action recognition datasets consist of collections of brief,trimmed videos each containing a relevant action, videos in the real-world(e.g., on YouTube) exhibit very different properties: they are often severalminutes long, where brief relevant clips are often interleaved with segments ofextended duration containing little change. Applying densely an actionrecognition system to every temporal clip within such videos is prohibitivelyexpensive. Furthermore, as we show in our experiments, this results insuboptimal recognition accuracy as informative predictions from relevant clipsare outnumbered by meaningless classification outputs over long uninformativesections of the video. In this paper we introduce a lightweight "clip-sampling"model that can efficiently identify the most salient temporal clips within along video. We demonstrate that the computational cost of action recognition onuntrimmed videos can be dramatically reduced by invoking recognition only onthese most salient clips. Furthermore, we show that this yields significantgains in recognition accuracy compared to analysis of all clips orrandomly/uniformly selected clips. On Sports1M, our clip sampling schemeelevates the accuracy of an already state-of-the-art action classifier by 7%and reduces by more than 15 times its computational cost.

SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition