Methods › Computer Vision › Semantic Segmentation Models › SegNet

SegNet

81 papers tagged archive 2025-07-28

Introduced by Vijay Badrinarayanan et al. in SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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

SegNet is a semantic segmentation model. This core trainable segmentation architecture consists of an encoder network, a corresponding decoder network followed by a pixel-wise classification layer. The architecture of the encoder network is topologically identical to the 13 convolutional layers in the VGG16 network. The role of the decoder network is to map the low resolution encoder feature maps to full input resolution feature maps for pixel-wise classification. The novelty of SegNet lies is in the manner in which the decoder upsamples its lower resolution input feature maps. Specifically, the decoder uses pooling indices computed in the max-pooling step of the corresponding encoder to perform non-linear upsampling.

PaperSourceSee Code · yassouali/pytorch_segmentation

Papers archive 2025-07-28

30 shown of 81, 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 92 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 Segmentation47
Segmentation39
Image Segmentation14
Decoder13
Image Classification7
image-classification7
Autonomous Driving5
Deep Learning5
General Classification5
Medical Image Analysis5
Object Detection5
Scene Understanding5
object-detection5
Data Augmentation4
Medical Image Segmentation4
Classification3
Crowd Counting3
Lesion Segmentation3
Boundary Detection2
Decision Making2

Usage over time archive 2025-07-28

Papers per year tagged with SegNet: 2015 to 2025, peak 15 15 0 2015: 2 papers 2015 2016: 2 papers 2016 2017: 4 papers 2017 2018: 13 papers 2018 2019: 11 papers 2019 2020: 15 papers 2020 2021: 3 papers 2021 2022: 6 papers 2022 2023: 9 papers 2023 2024: 10 papers 2024 2025: 6 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (81 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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