6 accepted papers
In the progress of industrial anomaly detection, general anomaly detection (GAD) is an emerging trend and also the ultimate goal. Unlike the conventional single- and multi-class AD, general AD aims to train a general AD model that can directly detect anomalies in diverse novel classes without any re
The current mainstream and state-of-the-art anomaly detection (AD) methods are substantially established on pretrained feature networks yielded by ImageNet pre- training. However, regardless of supervised or self-supervised pretraining, the pretraining process on ImageNet does not match the goal of…
Few-shot action recognition (FSAR) aims to classify human actions in videos with only a small number of labeled samples per category. The scarcity of training data has driven recent efforts to incorporate additional modalities, particularly text. However, the subtle variations in human posture, moti…
In autonomous driving, the ability to detect pedestrians accurately is crucial for safety. Some detectors, however, often struggle with occlusions, where pedestrians partially hidden behind objects appear incomplete and are harder to be identified accurately. To alleviate this issue, we introduce Cl…
"Unified anomaly detection (AD) is one of the most valuable challenges for anomaly detection, where one unified model is trained with normal samples from multiple classes with the objective to detect anomalies in these classes. For such a challenging task, popular normalizing flow (NF) based AD meth…
Humans recognize anomalies through two aspects: larger patch-wise representation discrepancies and weaker patch-to-normal-patch correlations. However, the previous AD methods didn't sufficiently combine the two complementary aspects to design AD models. To this end, we find that Transformer can idea…