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We present Accel, a novel semantic video segmentation system that achieveshigh accuracy at low inference cost by combining the predictions of two networkbranches: (1) a reference branch that extracts high-detail features on areference keyframe, and warps these features forward using frame-to-frameoptical flow estimates, and (2) an update branch that computes features ofadjustable quality on the current frame, performing a temporal update at eachvideo frame. The modularity of the update branch, where feature subnetworks ofvarying layer depth can be inserted (e.g. ResNet-18 to ResNet-101), enablesoperation over a new, state-of-the-art accuracy-throughput trade-off spectrum.Over this curve, Accel models achieve both higher accuracy and faster inferencetimes than the closest comparable single-frame segmentation networks. Ingeneral, Accel significantly outperforms previous work on efficient semanticvideo segmentation, correcting warping-related error that compounds on datasetswith complex dynamics. Accel is end-to-end trainable and highly modular: thereference network, the optical flow network, and the update network can each beselected independently, depending on application requirements, and then jointlyfine-tuned. The result is a robust, general system for fast, high-accuracysemantic segmentation on video.

Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video