4 citations · 8 across the 5 of their papers we have counts for
11 papers
Industrial Anomaly Detection via Defect-Grounded Reasoning in Visual Latent Space
Jaron Yeh, Yen-Wei Chang, Jiang Liu +1
Industrial anomaly detection (IAD) is evolving beyond conventional detection and localization toward multimodal inspection systems that can describe, explain, and reason about fine…
Towards Zero-Shot Anomaly Detection and Reasoning with Multimodal Large Language Models
Jiacong Xu, Shao-Yuan Lo, Bardia Safaei +2
Zero-Shot Anomaly Detection (ZSAD) is an emerging AD paradigm. Unlike the traditional unsupervised AD setting that requires a large number of normal samples to train a model, ZSAD…
Adaptive Batch Normalization Networks for Adversarial Robustness
Shao-Yuan Lo, Vishal M. Patel
Deep networks are vulnerable to adversarial examples. Adversarial Training (AT) has been a standard foundation of modern adversarial defense approaches due to its remarkable effect…
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation
Shao-Yuan Lo, Poojan Oza, Sumanth Chennupati +2
Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target da…
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation
Shaoyan Pan, Shao-Yuan Lo, Min Huang +5
In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR)…
Error Diffusion Halftoning Against Adversarial Examples
Shao-Yuan Lo, Vishal M. Patel
Adversarial examples contain carefully crafted perturbations that can fool deep neural networks (DNNs) into making wrong predictions. Enhancing the adversarial robustness of DNNs h…