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FASTER Recurrent Networks for Efficient Video Classification
[article]
2019
arXiv
pre-print
Typical video classification methods often divide a video into short clips, do inference on each clip independently, then aggregate the clip-level predictions to generate the video-level results. However, processing visually similar clips independently ignores the temporal structure of the video sequence, and increases the computational cost at inference time. In this paper, we propose a novel framework named FASTER, i.e., Feature Aggregation for Spatio-TEmporal Redundancy. FASTER aims to
arXiv:1906.04226v2
fatcat:45mfn6rmjrc55km56vxbrjbiyu