Face recognition (FR) is one of the most popular and long-standing topics in computer
vision. With the recent development of deep learning techniques and large-scale datasets …

In this paper, we present an accurate and scalable approach to the face clustering task. We
aim at grou** a set of faces by their potential identities. We formulate this task as a link …

Face recognition sees remarkable progress in recent years, and its performance has
reached a very high level. Taking it to a next level requires substantially larger data, which …

Face clustering is an essential tool for exploiting the unlabeled face data, and has a wide
range of applications including face annotation and retrieval. Recent works show that …

Deep clustering gains superior performance than conventional clustering by jointly
performing feature learning and cluster assignment. Although numerous deep clustering …

Document image enhancement methods are often used to improve the accuracy and
efficiency of automated document analysis and recognition tasks such as character …

Recently, deep clustering, which is able to perform feature learning that favors clustering
tasks via deep neural networks, has achieved remarkable performance in image clustering …

One of the most important yet rarely studied challenges for supervised face clustering is the
large intra-class variance caused by different face attributes such as age, pose, and …

We propose a hierarchical graph neural network (GNN) model that learns how to cluster a
set of images into an unknown number of identities using a training set of images annotated …

Face clustering is a promising method for annotating unlabeled face images. Recent
supervised approaches have boosted the face clustering accuracy greatly, however their …