6 papers · 1 filter
Multi-Scale Distillation for RGB-D Anomaly Detection on the PD-REAL Dataset
Jianjian Qin, Chao Zhang, Chunzhi Gu +5
We present PD-REAL, a novel large-scale dataset for unsupervised anomaly detection (AD) in the 3D domain. It is motivated by the fact that 2D-only representations in the AD task ma…
DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samples
Zi Wang, Katsuya Hotta, Koichiro Kamide +4
Cross-category anomaly detection for 3D point clouds aims to determine whether an unseen object belongs to a target category using only a few normal examples. Most existing methods…
3DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clustering
Zi Wang, Katsuya Hotta, Koichiro Kamide +3
High-resolution 3D point clouds are highly effective for detecting subtle structural anomalies in industrial inspection. However, their dense and irregular nature imposes significa…
Dataset Distillation via Vision-Language Category Prototype
Yawen Zou, Guang Li, Duo Su +3
Dataset distillation (DD) condenses large datasets into compact yet informative substitutes, preserving performance comparable to the original dataset while reducing storage, trans…
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…