Papers › RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer
Jian Wang, Chenhui Gou, Qiman Wu, Haocheng Feng, Junyu Han, Errui Ding, Jingdong Wang
Recently, transformer-based networks have shown impressive results in semantic segmentation. Yet for real-time semantic segmentation, pure CNN-based approaches still dominate in this field, due to the time-consuming computation mechanism of transformer. We propose RTFormer, an efficient dual-resolution transformer for real-time semantic segmenation, which achieves better trade-off between performance and efficiency than CNN-based models. To achieve high inference efficiency on GPU-like devices, our RTFormer leverages GPU-Friendly Attention with linear complexity and discards the multi-head mechanism. Besides, we find that cross-resolution attention is more efficient to gather global context information for high-resolution branch by spreading the high level knowledge learned from low-resolution branch. Extensive experiments on mainstream benchmarks demonstrate the effectiveness of our proposed RTFormer, it achieves state-of-the-art on Cityscapes, CamVid and COCOStuff, and shows promising results on ADE20K. Code is available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Real-Time Semantic Segmentation | CamVid | RTFormer-Slim | Frame (fps) | 190.7(2080Ti) | #2 of 29 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | CamVid | RTFormer-Slim | mIoU | 81.4 | #2 of 29 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | Cityscapes val | RTFormer-B | Frame (fps) | 50.2 | #6 of 24 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | Cityscapes val | RTFormer-B | mIoU | 79.3% | #6 of 24 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | Cityscapes val | RTFormer-S | Frame (fps) | 89.6 | #13 of 24 | Archive leaderboard | report |
| Real-Time Semantic Segmentation | Cityscapes val | RTFormer-S | mIoU | 76.3% | #13 of 24 | Archive leaderboard | report |
| Semantic Segmentation | CamVid | RTFormer-Base | Mean IoU | 82.5 | #4 of 21 | 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.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections