**ZFNet** is a classic convolutional neural network. The design was motivated by visualizing intermediate feature layers and the operation of the classifier. Compared to [AlexNet](https://paperswithcode.com/method/alexnet), the filter sizes are reduced and the stride of the convolutions are reduced.

**Adaptive Bezier-Curve Network**, or **ABCNet**, is an end-to-end framework for arbitrarily-shaped scene text spotting. It adaptively fits arbitrary-shaped text by a parameterized bezier curve. It also utilizes a feature alignment layer, [BezierAlign](https://paperswithcode.com/method/bezieralign), to calculate convolutional features of text instances in curved shapes. These features are then passed to a light-weight recognition head.

ABCNet

ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

ZFNet

Visualizing and Understanding Convolutional Networks

**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map for each corresponding category of the classification task in the last mlpconv layer. Instead of adding fully connected layers on top of the feature maps, we take the average of each feature map, and the resulting vector is fed directly into the [softmax](https://paperswithcode.com/method/softmax) layer. 

One advantage of global [average pooling](https://paperswithcode.com/method/average-pooling) over the fully connected layers is that it is more native to the [convolution](https://paperswithcode.com/method/convolution) structure by enforcing correspondences between feature maps and categories. Thus the feature maps can be easily interpreted as categories confidence maps. Another advantage is that there is no parameter to optimize in the global average pooling thus overfitting is avoided at this layer. Furthermore, global average pooling sums out the spatial information, thus it is more robust to spatial translations of the input.

Source	Visualizing and Understanding Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com