4 citations · 6 across the 6 of their papers we have counts for
7 papers
LogiCo: A Unified Framework for Logical and Structural Anomaly Detection
Ximiao Zhang, Min Xu, Xiuzhuang Zhou
Current anomaly detection methods primarily focus on structural anomalies, while paying insufficient attention to anomalies that violate logical constraints. Conversely, top-perfor…
RASLF: Representation-Aware State Space Model for Light Field Super-Resolution
Zeqiang Wei, Kai Jin, Kuan Song +3
Current SSM-based light field super-resolution (LFSR) methods often fail to fully leverage the complementarity among various LF representations, leading to the loss of fine texture…
UniADC: A Unified Framework for Anomaly Detection and Classification
Ximiao Zhang, Min Xu, Zheng Zhang +2
In this paper, we introduce a novel task termed unified anomaly detection and classification, which aims to simultaneously detect anomalous regions in images and identify their spe…
Towards High-Resolution Industrial Image Anomaly Detection
Ximiao Zhang, Min Xu, Xiuzhuang Zhou
Current anomaly detection methods primarily focus on low-resolution scenarios. For high-resolution images, conventional downsampling often results in missed detections of subtle an…
MediCLIP: Adapting CLIP for Few-shot Medical Image Anomaly Detection
Ximiao Zhang, Min Xu, Dehui Qiu +3
In the field of medical decision-making, precise anomaly detection in medical imaging plays a pivotal role in aiding clinicians. However, previous work is reliant on large-scale da…
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection
Ximiao Zhang, Min Xu, Xiuzhuang Zhou
Self-supervised feature reconstruction methods have shown promising advances in industrial image anomaly detection and localization. Despite this progress, these methods still face…
Weakly Supervised Anomaly Detection for Chest X-Ray Image
Haoqi Ni, Ximiao Zhang, Min Xu +2
Chest X-Ray (CXR) examination is a common method for assessing thoracic diseases in clinical applications. While recent advances in deep learning have enhanced the significance of…