Deep metric learning papers from the past four years have consistently claimed great
advances in accuracy, often more than doubling the performance of decade-old methods. In …

Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs
from sets of negative samples. Recently, the principle has also been used to learn cross …

Metric learning aims to learn a highly discriminative model encouraging the embeddings of
similar classes to be close in the chosen metrics and pushed apart for dissimilar ones. The …

Transformers have shown outstanding results for natural language understanding and, more
recently, for image classification. We here extend this work and propose a transformer …

Abstract The goal of Facial Kinship Verification (FKV) is to automatically determine whether
two individuals have a kin relationship or not from their given facial images or videos. It is an …

Mining informative negative instances are of central importance to deep metric learning
(DML). However, the hard-mining ability of existing DML methods is intrinsically limited by …

Optimising a ranking-based metric, such as Average Precision (AP), is notoriously
challenging due to the fact that it is non-differentiable, and hence cannot be optimised …

The whole slide image (WSI) classification is often formulated as a multiple instance
learning (MIL) problem. Since the positive tissue is only a small fraction of the gigapixel WSI …

The objective of deep metric learning (DML) is to learn embeddings that can capture
semantic similarity information among data points. Existing pairwise or tripletwise loss …

Contrastive learning (CL) is a popular technique for self-supervised learning (SSL) of visual
representations. It uses pairs of augmentations of unlabeled training examples to define a …