# GSVNet: Guided Spatially-Varying Convolution for Fast Semantic Segmentation on Video

Source: [GSVNet: Guided Spatially-Varying Convolution for Fast Semantic Segmentation on Video](<https://www.sota2.com/research/paper/68fa6a241e209131cea2651e>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:17:37.192Z

Authors: Shih-Po Lee, Si-Cun Chen, Wen-Hsiao Peng

Published: 2021-03-16T03:38:59.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2103.08834>)

[PDF](<https://arxiv.org/pdf/2103.08834.pdf>)