Methods › Computer Vision › Semantic Segmentation Models › PSPNet

PSPNet

47 papers tagged archive 2025-07-28

Introduced by Hengshuang Zhao et al. in Pyramid Scene Parsing Network

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

PSPNet, or Pyramid Scene Parsing Network, is a semantic segmentation model that utilises a pyramid parsing module that exploits global context information by different-region based context aggregation. The local and global clues together make the final prediction more reliable. We also propose an optimization

Given an input image, PSPNet use a pretrained CNN with the dilated network strategy to extract the feature map. The final feature map size is 1/8 of the input image. On top of the map, we use the pyramid pooling module to gather context information. Using our 4-level pyramid, the pooling kernels cover the whole, half of, and small portions of the image. They are fused as the global prior. Then we concatenate the prior with the original feature map in the final part of. It is followed by a convolution layer to generate the final prediction map.

PaperSourceSee Code · xitongpu/PSPNet

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 54 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 Segmentation41
Segmentation26
Image Segmentation8
Autonomous Driving7
Scene Parsing6
Image Classification5
Scene Understanding5
Decoder4
Instance Segmentation4
image-classification4
Deep Learning3
Few-Shot Semantic Segmentation3
Knowledge Distillation3
Object Detection3
object-detection3
General Classification2
Generalized Few-Shot Semantic Segmentation2
Lesion Segmentation2
Object2
Real-Time Semantic Segmentation2

Usage over time archive 2025-07-28

Papers per year tagged with PSPNet: 2016 to 2025, peak 9 9 0 2016: 1 paper 2016 2017: 0 papers 2017 2018: 5 papers 2018 2019: 2 papers 2019 2020: 6 papers 2020 2021: 9 papers 2021 2022: 6 papers 2022 2023: 8 papers 2023 2024: 7 papers 2024 2025: 3 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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