Papers › Multi-Scale Context Aggregation by Dilated Convolutions
Multi-Scale Context Aggregation by Dilated Convolutions
Fisher Yu, Vladlen Koltun
State-of-the-art models for semantic segmentation are based on adaptations of convolutional networks that had originally been designed for image classification. However, dense prediction and image classification are structurally different. In this work, we develop a new convolutional network module that is specifically designed for dense prediction. The presented module uses dilated convolutions to systematically aggregate multi-scale contextual information without losing resolution. The architecture is based on the fact that dilated convolutions support exponential expansion of the receptive field without loss of resolution or coverage. We show that the presented context module increases the accuracy of state-of-the-art semantic segmentation systems. In addition, we examine the adaptation of image classification networks to dense prediction and show that simplifying the adapted network can increase accuracy.
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Code
Syntology Ran 2 of 8 code samples harvested from 3 repositories linked to this paper; 6 have no recorded run. Of those that ran: 1 ran · honoured contract; 1 ran · our draft was wrong.
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Code Syntology ran Syntology
8 samples harvested; 2 ran; 1 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Real-Time Semantic Segmentation | CamVid | Dilation10 | Frame (fps) | 4.4 | #25 of 29 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | CamVid | Dilation10 | Time (ms) | 227 | #25 of 29 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | CamVid | Dilation10 | mIoU | 65.3% | #25 of 29 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | DilatedNet | Validation mIoU | 32.31 | #228 of 235 | Archive leaderboard | report |
| Semantic Segmentation | CamVid | Dilated Convolutions | Mean IoU | 65.3% | #14 of 21 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | Dilation10 | Mean IoU (class) | 67.1% | #90 of 105 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL VOC 2012 test | Dilated Convolutions | Mean IoU | 67.6% | #42 of 51 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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