Distance metric learning is a branch of machine learning that aims to learn distances from
the data, which enhances the performance of similarity-based algorithms. This tutorial …

Exploring good generalization ability is essential in deep metric learning (DML). Most
existing DML methods focus on improving the model robustness against category shift to …

Learning discriminative features is crucial for hyperspectral image (HSI) classification.
Though metric learning has been applied to learn effective features in HSI classification …

Most existing cross-modality person Re-IDentification works rely on discriminative modality-
shared features for reducing cross-modality variations and intra-modality variations. Despite …

With the development of deep learning techniques, fusion of deep features has
demonstrated the powerful capability to improve recognition performance. However, most …

Distance metric learning aims to deal with the data distribution by learning a suitable
distance metric from the training instances. For distance metric learning, the optimization …

Existing part-aware person re-identification methods typically employ two separate steps:
namely, body part detection and part-level feature extraction. However, part detection …

Cross-modality visible-Infrared person re-identification (cm-ReID) is extremely challenging
due to the huge modality discrepancy between RGB and IR modalities. Existing methods …

Devices such as smartphones and tablets have spurred interest in off-the-person
electrocardiogram (ECG) biometric recognition. While the advantage of using multi-feature …

Deep Metric Learning (DML) plays a critical role in various machine learning tasks.
However, most existing deep metric learning methods with binary similarity are sensitive to …