Image Model Blocks

Global Context Block

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

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 $W_{k}$ 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 $W_{v}$; (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.

Source: GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond


Paper Code Results Date Stars


Task Papers Share
Object Detection 3 20.00%
Stereo Matching 2 13.33%
Instance Segmentation 2 13.33%
Point Cloud Registration 1 6.67%
Metric Learning 1 6.67%
Robot Navigation 1 6.67%
Management 1 6.67%
Multi-Object Tracking 1 6.67%
Object Tracking 1 6.67%