This paper proposes a Positional-Encoded Asynchronous AutoRegression (PEAR) method
for satellite anomaly detection. We empirically observe that a single classification model can …

Deep Metric Learning (DML) loss functions traditionally aim to control the forces of
separability and compactness within an embedding space so that the same class data …

Deep Metric Learning (DML) has long attracted the attention of the machine learning
community as a key objective. Existing solutions concentrate on fine-tuning the pre-trained …

Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding
space for downstream tasks like classification, clustering, and retrieval. Prior literature …

Hard negative generation aims to generate informative negative samples that help to
determine the decision boundaries and thus facilitate advancing deep metric learning …

This paper proposes an introspective deep metric learning (IDML) framework for uncertainty-
aware comparisons of images. Conventional deep metric learning methods focus on …

Deep metric learning (DML) involves training a network to learn a semantically meaningful
representation space. Many current approaches mine n-tuples of examples and model …

Deep learning has seen remarkable advancements in machine learning, yet it often
demands extensive annotated data. Tasks like 3D semantic segmentation impose a …

Fine-grained image retrieval (FGIR) is to learn visual representations that distinguish
visually similar objects while maintaining generalization. Existing methods propose to …

Deep metric learning (DML) aims to learn a discriminative high-dimensional embedding
space for downstream tasks like classification, clustering, and retrieval. Prior literature …