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The paucity of videos in current action classification datasets (UCF-101 andHMDB-51) has made it difficult to identify good video architectures, as mostmethods obtain similar performance on existing small-scale benchmarks. Thispaper re-evaluates state-of-the-art architectures in light of the new KineticsHuman Action Video dataset. Kinetics has two orders of magnitude more data,with 400 human action classes and over 400 clips per class, and is collectedfrom realistic, challenging YouTube videos. We provide an analysis on howcurrent architectures fare on the task of action classification on this datasetand how much performance improves on the smaller benchmark datasets afterpre-training on Kinetics.  We also introduce a new Two-Stream Inflated 3D ConvNet (I3D) that is based on2D ConvNet inflation: filters and pooling kernels of very deep imageclassification ConvNets are expanded into 3D, making it possible to learnseamless spatio-temporal feature extractors from video while leveragingsuccessful ImageNet architecture designs and even their parameters. We showthat, after pre-training on Kinetics, I3D models considerably improve upon thestate-of-the-art in action classification, reaching 80.9% on HMDB-51 and 98.0%on UCF-101.

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset