Methods › Computer Vision › Semantic Segmentation Models › FCN

Fully Convolutional Network

FCN

285 papers tagged archive 2025-07-28

Introduced by Evan Shelhamer et al. in Fully Convolutional Networks for Semantic Segmentation

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

Fully Convolutional Networks, or FCNs, are an architecture used mainly for semantic segmentation. They employ solely locally connected layers, such as convolution, pooling and upsampling. Avoiding the use of dense layers means less parameters (making the networks faster to train). It also means an FCN can work for variable image sizes given all connections are local.

The network consists of a downsampling path, used to extract and interpret the context, and an upsampling path, which allows for localization.

FCNs also employ skip connections to recover the fine-grained spatial information lost in the downsampling path.

PaperSourceSee Code · Jackey9797/FCN

Papers archive 2025-07-28

30 shown of 285, 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 236 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
Segmentation112
Semantic Segmentation101
Image Segmentation44
Object Detection24
object-detection22
General Classification20
Medical Image Segmentation20
Decoder18
Image Classification15
Object13
image-classification13
Classification9
Time Series9
Transfer Learning9
Medical Image Analysis8
Tumor Segmentation8
regression8
Autonomous Driving7
Deep Learning7
Denoising7

Usage over time archive 2025-07-28

Papers per year tagged with FCN: 2015 to 2025, peak 49 49 0 2015: 1 paper 2015 2016: 22 papers 2016 2017: 36 papers 2017 2018: 41 papers 2018 2019: 49 papers 2019 2020: 42 papers 2020 2021: 26 papers 2021 2022: 26 papers 2022 2023: 21 papers 2023 2024: 19 papers 2024 2025: 2 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (285 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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