5 papers
VID-AD: A Dataset for Image-Level Logical Anomaly Detection under Vision-Induced Distraction
Hiroto Nakata, Yawen Zou, Shunsuke Sakai +5
Logical anomaly detection in industrial inspection remains challenging due to variations in visual appearance (e.g., background clutter, illumination shift, and blur), which often…
3D Human-Human Interaction Anomaly Detection
Shun Maeda, Chunzhi Gu, Koichiro Kamide +3
Human-centric anomaly detection (AD) has been primarily studied to specify anomalous behaviors in a single person. However, as humans by nature tend to act in a collaborative manne…
Incremental Pseudo-Labeling for Black-Box Unsupervised Domain Adaptation
Yawen Zou, Chunzhi Gu, Jun Yu +2
Black-Box unsupervised domain adaptation (BBUDA) learns knowledge only with the prediction of target data from the source model without access to the source data and source model,…
Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows
Shun Maeda, Chunzhi Gu, Jun Yu +3
We introduce the task of human action anomaly detection (HAAD), which aims to identify anomalous motions in an unsupervised manner given only the pre-determined normal category of…
A Multi-In and Multi-Out Dendritic Neuron Model and its Optimization
Yu Ding, Jun Yu, Chunzhi Gu +2
Artificial neural networks (ANNs), inspired by the interconnection of real neurons, have achieved unprecedented success in various fields such as computer vision and natural langua…