5 citations · 8 across the 6 of their papers we have counts for
6 papers
Commonality in Few: Few-Shot Multimodal Anomaly Detection via Hypergraph-Enhanced Memory
Yuxuan Lin, Hanjing Yan, Xuan Tong +6
Few-shot multimodal industrial anomaly detection is a critical yet underexplored task, offering the ability to quickly adapt to complex industrial scenarios. In few-shot settings,…
Noise Fusion-based Distillation Learning for Anomaly Detection in Complex Industrial Environments
Jiawen Yu, Jieji Ren, Yang Chang +7
Anomaly detection and localization in automated industrial manufacturing can significantly enhance production efficiency and product quality. Existing methods are capable of detect…
Component-aware Unsupervised Logical Anomaly Generation for Industrial Anomaly Detection
Xuan Tong, Yang Chang, Qing Zhao +9
Anomaly detection is critical in industrial manufacturing for ensuring product quality and improving efficiency in automated processes. The scarcity of anomalous samples limits tra…
A Survey on RGB, 3D, and Multimodal Approaches for Unsupervised Industrial Image Anomaly Detection
Yuxuan Lin, Yang Chang, Xuan Tong +8
In the advancement of industrial informatization, unsupervised anomaly detection technology effectively overcomes the scarcity of abnormal samples and significantly enhances the au…
A Survey on Facial Expression Recognition of Static and Dynamic Emotions
Yan Wang, Shaoqi Yan, Yang Liu +7
Facial expression recognition (FER) aims to analyze emotional states from static images and dynamic sequences, which is pivotal in enhancing anthropomorphic communication among hum…
From Efficient Multimodal Models to World Models: A Survey
Xinji Mai, Zeng Tao, Junxiong Lin +5
Multimodal Large Models (MLMs) are becoming a significant research focus, combining powerful large language models with multimodal learning to perform complex tasks across differen…