Global Context Block
Global Context Block
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](https://paperswithcode.com/method/non-local-block) with effective modeling of long-range dependencies, and the [squeeze-excitation block](https://paperswithcode.com/method/squeeze-and-excitation-block) with lightweight computation. 

In the Global Context framework, we have (a) global attention pooling, which adopts a [1x1 convolution](https://paperswithcode.com/method/1x1-convolution) $W_{k}$ and [softmax](https://paperswithcode.com/method/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](https://paperswithcode.com/method/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.
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
https://paperswithcode.com/paper/gcnet-non-local-networks-meet-squeeze
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond