Papers › ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time

ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time

11 May 2018arXiv:1805.04554archive 2025-07-28

Rudra P. K. Poudel, Ujwal Bonde, Stephan Liwicki, Christopher Zach

Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transferable to real-time applications or embedded devices, since naive adaptation of such systems to reduce computational cost (speed, memory and energy) causes a significant drop in accuracy. We propose ContextNet, a new deep neural network architecture which builds on factorized convolution, network compression and pyramid representation to produce competitive semantic segmentation in real-time with low memory requirement. ContextNet combines a deep network branch at low resolution that captures global context information efficiently with a shallow branch that focuses on high-resolution segmentation details. We analyse our network in a thorough ablation study and present results on the Cityscapes dataset, achieving 66.1% accuracy at 18.3 frames per second at full (1024x2048) resolution (41.9 fps with pipelined computations for streamed data).

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osmr/imgclsmob mentioned on GitHubmxnetMIT report

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SegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semantic Segmentation Cityscapes val ContextNet mIoU 65.9% #92 of 99 Archive leaderboard report

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