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In this paper, we are interested in self-supervised learning the motion cuesin videos using dynamic motion filters for a better motion representation tofinally boost human action recognition in particular. Thus far, the visioncommunity has focused on spatio-temporal approaches using standard filters,rather we here propose dynamic filters that adaptively learn the video-specificinternal motion representation by predicting the short-term future frames. Wename this new motion representation, as dynamic motion representation (DMR) andis embedded inside of 3D convolutional network as a new layer, which capturesthe visual appearance and motion dynamics throughout entire video clip viaend-to-end network learning. Simultaneously, we utilize these motionrepresentation to enrich video classification. We have designed the frameprediction task as an auxiliary task to empower the classification problem.With these overall objectives, to this end, we introduce a novel unifiedspatio-temporal 3D-CNN architecture (DynamoNet) that jointly optimizes thevideo classification and learning motion representation by predicting futureframes as a multi-task learning problem. We conduct experiments on challenginghuman action datasets: Kinetics 400, UCF101, HMDB51. The experiments using theproposed DynamoNet show promising results on all the datasets.

DynamoNet: Dynamic Action and Motion Network