Methods › Computer Vision › Semantic Segmentation Models › DeepLabv2

DeepLabv2

4 papers tagged archive 2025-07-28

Introduced by Liang-Chieh Chen et al. in DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

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

DeepLabv2 is an architecture for semantic segmentation that build on DeepLab with an atrous spatial pyramid pooling scheme. Here we have parallel dilated convolutions with different rates applied in the input feature map, which are then fused together. As objects of the same class can have different sizes in the image, ASPP helps to account for different object sizes.

PaperSourceSee Code · tensorflow/models

Papers archive 2025-07-28

4 shown of 4, 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

7 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 Segmentation4
Domain Adaptation2
Segmentation2
Unsupervised Domain Adaptation2
Data Augmentation1
Image Segmentation1
Semi-Supervised Semantic Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with DeepLabv2: 2016 to 2024, peak 1 1 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 0 papers 2018 2019: 0 papers 2019 2020: 1 paper 2020 2021: 0 papers 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (4 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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