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In this paper, we propose a framework named OCSampler to explore a compactyet effective video representation with one short clip for efficient videorecognition. Recent works prefer to formulate frame sampling as a sequentialdecision task by selecting frames one by one according to their importance,while we present a new paradigm of learning instance-specific videocondensation policies to select informative frames for representing the entirevideo only in a single step. Our basic motivation is that the efficient videorecognition task lies in processing a whole sequence at once rather thanpicking up frames sequentially. Accordingly, these policies are derived from alight-weighted skim network together with a simple yet effective policy networkwithin one step. Moreover, we extend the proposed method with a frame numberbudget, enabling the framework to produce correct predictions in highconfidence with as few frames as possible. Experiments on four benchmarks,i.e., ActivityNet, Mini-Kinetics, FCVID, Mini-Sports1M, demonstrate theeffectiveness of our OCSampler over previous methods in terms of accuracy,theoretical computational expense, actual inference speed. We also evaluate itsgeneralization power across different classifiers, sampled frames, and searchspaces. Especially, we achieve 76.9% mAP and 21.7 GFLOPs on ActivityNet with animpressive throughput: 123.9 Videos/s on a single TITAN Xp GPU.

OCSampler: Compressing Videos to One Clip with Single-step Sampling