12 citations · 17 across the 6 of their papers we have counts for
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3 papers · 1 filter
cs.CV2022
CobNet: Cross Attention on Object and Background for Few-Shot Segmentation
Haoyan Guan, Michael Spratling
Few-shot segmentation aims to segment images containing objects from previously unseen classes using only a few annotated samples. Most current methods focus on using object inform…
cs.CV2022
Query Semantic Reconstruction for Background in Few-Shot Segmentation
Haoyan Guan, Michael Spratling
Few-shot segmentation (FSS) aims to segment unseen classes using a few annotated samples. Typically, a prototype representing the foreground class is extracted from annotated suppo…
cs.CV2022★ 3 cited
Registration based Few-Shot Anomaly Detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang +3
This paper considers few-shot anomaly detection (FSAD), a practical yet under-studied setting for anomaly detection (AD), where only a limited number of normal images are provided…