**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.

Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL.

DualCL

Dual Contrastive Learning: Text Classification via Label-Aware Data Augmentation

ABCNet

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

**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.

Source	ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network
Year	2000
Data Source	Papers with Code archive (CC BY-SA)