Methods › General › Skip Connection Blocks › Global Context Block

Global Context Block

12 papers tagged archive 2025-07-28

Introduced by Yue Cao et al. in GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

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

A Global Context Block is an image model block for global context modeling. The aim is to have both the benefits of the simplified non-local block with effective modeling of long-range dependencies, and the squeeze-excitation block with lightweight computation.

In the Global Context framework, we have (a) global attention pooling, which adopts a 1x1 convolution Wₖ and softmax function to obtain the attention weights, and then performs the attention pooling to obtain the global context features, (b) feature transform via a 1x1 convolution Wᵥ; (c) feature aggregation, which employs addition to aggregate the global context features to the features of each position. Taken as a whole, the GC block is proposed as a lightweight way to achieve global context modeling.

PaperSourceSee Code · xvjiarui/GCNet

Papers archive 2025-07-28

12 shown of 12, 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

18 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 Detection3
Decoder2
Instance Segmentation2
Stereo Matching2
Disparity Estimation1
Graph Neural Network1
Management1
Metric Learning1
Multi-Object Tracking1
Object1
Object Recognition1
Object Tracking1
Point Cloud Registration1
Prediction1
Real-Time Semantic Segmentation1
Robot Navigation1
Semantic Segmentation1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with Global Context Block: 2019 to 2025, peak 4 4 0 2019: 1 paper 2019 2020: 3 papers 2020 2021: 2 papers 2021 2022: 4 papers 2022 2023: 1 paper 2023 2024: 0 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (12 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

Skip Connection BlocksAttention ModulesImage Model Blocks

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