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Methods › Computer Vision › Feature Extractors › Bottom-up Path Augmentation

Bottom-up Path Augmentation

123 papers tagged archive 2025-07-28

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

Bottom-up Path Augmentation is a feature extraction technique that seeks to shorten the information path and enhance a feature pyramid with accurate localization signals existing in low-levels. This is based on the fact that high response to edges or instance parts is a strong indicator to accurately localize instances.

Each building block takes a higher resolution feature map Nᵢ and a coarser map Pᵢ₊₁ through lateral connection and generates the new feature map Nᵢ₊₁ Each feature map Nᵢ first goes through a 3 ×3 convolutional layer with stride $2$ to reduce the spatial size. Then each element of feature map Pᵢ₊₁ and the down-sampled map are added through lateral connection. The fused feature map is then processed by another 3 ×3 convolutional layer to generate Nᵢ₊₁ for following sub-networks. This is an iterative process and terminates after approaching P₅. In these building blocks, we consistently use channel 256 of feature maps. The feature grid for each proposal is then pooled from new feature maps, i.e., {N₂, N₃, N₄, N₅}.

Source: Path Aggregation Network for Instance SegmentationSee Code · ShuLiu1993/PANet

Papers archive 2025-07-28

30 shown of 123, 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 144 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
Object Detection69
object-detection63
Object33
Segmentation13
Real-Time Object Detection12
Semantic Segmentation12
GPU8
Autonomous Driving7
Data Augmentation7
Deep Learning7
Instance Segmentation7
2D Object Detection5
Image Classification5
Transfer Learning5
Small Object Detection4
Traffic Sign Detection4
image-classification4
Domain Adaptation3
Object Tracking3
Position3

Usage over time archive 2025-07-28

Papers per year tagged with Bottom-up Path Augmentation: 2018 to 2025, peak 33 33 0 2018: 1 paper 2018 2019: 4 papers 2019 2020: 18 papers 2020 2021: 28 papers 2021 2022: 33 papers 2022 2023: 21 papers 2023 2024: 10 papers 2024 2025: 8 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (123 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

Feature Extractors

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