Methods › Computer Vision › Semantic Segmentation Models › SegFormer

SegFormer

47 papers tagged archive 2025-07-28

Introduced by Enze Xie et al. in SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

SegFormer is a Transformer-based framework for semantic segmentation that unifies Transformers with lightweight multilayer perceptron (MLP) decoders. SegFormer has two appealing features: 1) SegFormer comprises a novel hierarchically structured Transformer encoder which outputs multiscale features. It does not need positional encoding, thereby avoiding the interpolation of positional codes which leads to decreased performance when the testing resolution differs from training. 2) SegFormer avoids complex decoders. The proposed MLP decoder aggregates information from different layers, and thus combining both local attention and global attention to render powerful representations.

PaperSource

Papers archive 2025-07-28

30 shown of 47, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 57 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Semantic Segmentation33
Segmentation22
Decoder7
Autonomous Driving4
Image Segmentation4
Data Augmentation3
Diversity3
GPU3
Image Classification3
Instance Segmentation3
Object Detection3
object-detection3
2D Semantic Segmentation2
Burned Area Delineation2
CPU2
Change Detection2
Disaster Response2
Land Cover Classification2
Object2
Prediction2

Usage over time archive 2025-07-28

Papers per year tagged with SegFormer: 2021 to 2025, peak 14 14 0 2021: 4 papers 2021 2022: 10 papers 2022 2023: 14 papers 2023 2024: 13 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (47 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Semantic Segmentation Models

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