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In this paper, we present a novel Single Shot multi-Span Detector fortemporal activity detection in long, untrimmed videos using a simple end-to-endfully three-dimensional convolutional (Conv3D) network. Our architecture, namedS3D, encodes the entire video stream and discretizes the output space oftemporal activity spans into a set of default spans over different temporallocations and scales. At prediction time, S3D predicts scores for the presenceof activity categories in each default span and produces temporal adjustmentsrelative to the span location to predict the precise activity duration. Unlikemany state-of-the-art systems that require a separate proposal andclassification stage, our S3D is intrinsically simple and dedicatedly designedfor single-shot, end-to-end temporal activity detection. When evaluating onTHUMOS'14 detection benchmark, S3D achieves state-of-the-art performance and isvery efficient and can operate at 1271 FPS.

S3D: Single Shot multi-Span Detector via Fully 3D Convolutional Networks