A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …

Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

Contrastive learning applied to self-supervised representation learning has seen a
resurgence in recent years, leading to state of the art performance in the unsupervised …

Recent self-supervised methods for image representation learning are based on maximizing
the agreement between embedding vectors from different views of the same image. A trivial …

One of the main challenges in feature learning using Deep Convolutional Neural Networks
(DCNNs) for large-scale face recognition is the design of appropriate loss functions that can …

Prevalent semantic segmentation solutions, despite their different network designs (FCN
based or attention based) and mask decoding strategies (parametric softmax based or pixel …

Face recognition has made extraordinary progress owing to the advancement of deep
convolutional neural networks (CNNs). The central task of face recognition, including face …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

Deep learning algorithms can fare poorly when the training dataset suffers from heavy class-
imbalance but the testing criterion requires good generalization on less frequent classes …

This paper addresses deep face recognition (FR) problem under open-set protocol, where
ideal face features are expected to have smaller maximal intra-class distance than minimal …