Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Anomaly detection with weakly supervised video-level labels is typically formulated as a
multiple instance learning (MIL) problem, in which we aim to identify snippets containing …

Video anomaly detection is well investigated in weakly supervised and one-class
classification (OCC) settings. However, unsupervised video anomaly detection is quite …

Object recognition requires a generalization capability to avoid overfitting, especially when
the samples are extremely few. Generalization from limited samples, usually studied under …

Weakly supervised detection of anomalies in surveillance videos is a challenging task.
Going beyond existing works that have deficient capabilities to localize anomalies in long …

Abstract Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the
binary anomaly label is only given on the video level, but the output requires snippet-level …

Anomaly detection in the video is an important research area and a challenging task in real
applications. Due to the unavailability of large-scale annotated anomaly events, most …

Video anomaly detection (VAD) aims at localizing unexpected actions or activities in a video
sequence. Existing mainstream VAD techniques are based on either the one-class …

图像异常检测是计算机视觉领域的一个热门研究课题, 其目标是在不使用真实异常样本的情况下
, 利用现有的正常样本构建模型以检测可能出现的各种异常图像, 在工业外观缺陷检测 …