Papers › SERNet-Former: Semantic Segmentation by Efficient Residual Network with...
SERNet-Former: Semantic Segmentation by Efficient Residual Network with Attention-Boosting Gates and Attention-Fusion Networks
Serdar Erisen
Improving the efficiency of state-of-the-art methods in semantic segmentation requires overcoming the increasing computational cost as well as issues such as fusing semantic information from global and local contexts. Based on the recent success and problems that convolutional neural networks (CNNs) encounter in semantic segmentation, this research proposes an encoder-decoder architecture with a unique efficient residual network, Efficient-ResNet. Attention-boosting gates (AbGs) and attention-boosting modules (AbMs) are deployed by aiming to fuse the equivariant and feature-based semantic information with the equivalent sizes of the output of global context of the efficient residual network in the encoder. Respectively, the decoder network is developed with the additional attention-fusion networks (AfNs) inspired by AbM. AfNs are designed to improve the efficiency in the one-to-one conversion of the semantic information by deploying additional convolution layers in the decoder part. Our network is tested on the challenging CamVid and Cityscapes datasets, and the proposed methods reveal significant improvements on the residual networks. To the best of our knowledge, the developed network, SERNet-Former, achieves state-of-the-art results (84.62 % mean IoU) on CamVid dataset and challenging results (87.35 % mean IoU) on Cityscapes validation dataset.
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
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 2D Semantic Segmentation | CamVid | SERNet-Former | mIoU | 84.62 | #1 of 1 | Archive leaderboard | report |
| 2D Semantic Segmentation | Cityscapes val | SERNet-Former | mIoU | 87.35 | #1 of 1 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | SERNet-Former | Validation mIoU | 59.35 | #17 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | SERNet-Former_v2 | mIoU | 59.35 | #8 of 95 | Archive leaderboard | report |
| Semantic Segmentation | BDD100K val | SERNet-Former_v2 | mIoU | 67.42 | #2 of 24 | Archive leaderboard | report |
| Semantic Segmentation | CamVid | SERNet-Former | Mean IoU | 84.62 | #1 of 21 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | SERNet-Former | Mean IoU (class) | 84.83 | #7 of 105 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | SERNet-Former | Validation mIoU | 87.35 | #2 of 99 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | SERNet-Former | mIoU | 87.35 | #2 of 99 | 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
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