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

Samvit Jain, Xin Wang, Joseph E. Gonzalez; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 8866-8875

Abstract


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

Related Material


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[bibtex]
@InProceedings{Jain_2019_CVPR,
author = {Jain, Samvit and Wang, Xin and Gonzalez, Joseph E.},
title = {Accel: A Corrective Fusion Network for Efficient Semantic Segmentation on Video},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}