This paper addresses fast semantic segmentation on video.Video segmentation
often calls for real-time, or even fasterthan real-time, processing. One common
recipe for conserving computation arising from feature extraction is to
propagate features of few selected keyframes. However, recent advances in fast
image segmentation make these solutions less attractive. To leverage fast image
segmentation for furthering video segmentation, we propose a simple yet
efficient propagation framework. Specifically, we perform lightweight flow
estimation in 1/8-downscaled image space for temporal warping in segmentation
outpace space. Moreover, we introduce a guided spatially-varying convolution
for fusing segmentations derived from the previous and current frames, to
mitigate propagation error and enable lightweight feature extraction on
non-keyframes. Experimental results on Cityscapes and CamVid show that our
scheme achieves the state-of-the-art accuracy-throughput trade-off on video
segmentation.

该论文提出了一种简单而有效的传播框架，包括轻量级流估计和引导式空间变化卷积方法，实现了视频分割的最优准确度和吞吐量的平衡。