2 citations · 3 across the 4 of their papers we have counts for
5 papers · 1 filter
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Jingqi Wu, Hanxi Li, Lin Yuanbo Wu +3
Industrial product inspection is often performed using Anomaly Detection (AD) frameworks trained solely on non-defective samples. Although defective samples can be collected during…
Self-Navigated Residual Mamba for Universal Industrial Anomaly Detection
Hanxi Li, Jingqi Wu, Lin Yuanbo Wu +4
In this paper, we propose Self-Navigated Residual Mamba (SNARM), a novel framework for universal industrial anomaly detection that leverages ``self-referential learning'' within te…
Boosting Box-supervised Instance Segmentation with Pseudo Depth
Xinyi Yu, Ling Yan, Pengtao Jiang +4
The realm of Weakly Supervised Instance Segmentation (WSIS) under box supervision has garnered substantial attention, showcasing remarkable advancements in recent years. However, t…
CTVIS: Consistent Training for Online Video Instance Segmentation
Kaining Ying, Qing Zhong, Weian Mao +7
The discrimination of instance embeddings plays a vital role in associating instances across time for online video instance segmentation (VIS). Instance embedding learning is direc…
Industrial Anomaly Detection and Localization Using Weakly-Supervised Residual Transformers
Hanxi Li, Jingqi Wu, Deyin Liu +4
Recent advancements in industrial anomaly detection (AD) have demonstrated that incorporating a small number of anomalous samples during training can significantly enhance accuracy…