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Spatial-temporal action detection is a vital part of video understanding.Current spatial-temporal action detection methods mostly use an object detectorto obtain person candidates and classify these person candidates into differentaction categories. So-called two-stage methods are heavy and hard to apply inreal-world applications. Some existing methods build one-stage pipelines, But alarge performance drop exists with the vanilla one-stage pipeline and extraclassification modules are needed to achieve comparable performance. In thispaper, we explore a simple and effective pipeline to build a strong one-stagespatial-temporal action detector. The pipeline is composed by two parts: one isa simple end-to-end spatial-temporal action detector. The proposed end-to-enddetector has minor architecture changes to current proposal-based detectors anddoes not add extra action classification modules. The other part is a novellabeling strategy to utilize unlabeled frames in sparse annotated data. Wenamed our model as SE-STAD. The proposed SE-STAD achieves around 2% mAP boostand around 80% FLOPs reduction. Our code will be released athttps://github.com/4paradigm-CV/SE-STAD.

A Simple and Efficient Pipeline to Build an End-to-End Spatial-Temporal Action Detector