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In this paper, we place the atomic action detection problem into a Long-ShortTerm Context (LSTC) to analyze how the temporal reliance among video signalsaffect the action detection results. To do this, we decompose the actionrecognition pipeline into short-term and long-term reliance, in terms of thehypothesis that the two kinds of context are conditionally independent giventhe objective action instance. Within our design, a local aggregation branch isutilized to gather dense and informative short-term cues, while a high orderlong-term inference branch is designed to reason the objective action classfrom high-order interaction between actor and other person or person pairs.Both branches independently predict the context-specific actions and theresults are merged in the end. We demonstrate that both temporal grains arebeneficial to atomic action recognition. On the mainstream benchmarks of atomicaction detection, our design can bring significant performance gain from theexisting state-of-the-art pipeline. The code of this project can be found at[this url](https://github.com/TencentYoutuResearch/ActionDetection-LSTC)

LSTC: Boosting Atomic Action Detection with Long-Short-Term Context