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We present AdaFrame, a framework that adaptively selects relevant frames on aper-input basis for fast video recognition. AdaFrame contains a Long Short-TermMemory network augmented with a global memory that provides context informationfor searching which frames to use over time. Trained with policy gradientmethods, AdaFrame generates a prediction, determines which frame to observenext, and computes the utility, i.e., expected future rewards, of seeing moreframes at each time step. At testing time, AdaFrame exploits predictedutilities to achieve adaptive lookahead inference such that the overallcomputational costs are reduced without incurring a decrease in accuracy.Extensive experiments are conducted on two large-scale video benchmarks, FCVIDand ActivityNet. AdaFrame matches the performance of using all frames with only8.21 and 8.65 frames on FCVID and ActivityNet, respectively. We furtherqualitatively demonstrate learned frame usage can indicate the difficulty ofmaking classification decisions; easier samples need fewer frames while harderones require more, both at instance-level within the same class and atclass-level among different categories.

AdaFrame: Adaptive Frame Selection for Fast Video Recognition