Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal behavior …

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-
ranging applications spanning machine learning, computer vision, natural language …

Knowledge Graph (KG) errors introduce non-negligible noise, severely affecting KG-related
downstream tasks. Detecting errors in KGs is challenging since the patterns of errors are …

This paper studies the challenging person re-identification (Re-ID) task under the cloth-
changing scenario, where the same identity (ID) suffers from uncertain cloth changes. To …

Anomaly detection (AD), aiming to find samples that deviate from the training distribution, is
essential in safety-critical applications. Though recent self-supervised learning based …

In addition to its success in representation learning, contrastive learning is effective in image
anomaly detection. Although contrastive learning depends significantly on data …

Rotation is frequently listed as a candidate for data augmentation in contrastive learning but
seldom provides satisfactory improvements. We argue that this is because the rotated image …

In cases such as digital enrolment via mobile and online services, identity document
verification is critical in order to efficiently detect forgery and therefore build user trust in the …

Despite significant progress in Anomaly Detection (AD), the robustness of existing detection
methods against adversarial attacks remains a challenge, compromising their reliability in …

Deep anomaly detection has recently seen significant developments to provide robust and
efficient classifiers using only a few anomalous samples. Many of those models consist in a …