3 papers
cs.CV2026
Rethinking Prototype-based Similarity Learning for Few-Shot Object Detection
KunHo Heo, Seungjae Kim, Wongyu Lee +2
Few-shot object detection aims to detect novel object categories from only a few labeled examples, avoiding costly large-scale annotation. Recent prototype-based similarity learnin…
cs.CV2026
A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detection
SuYeon Kim, Wongyu Lee, MyeongAh Cho
3D anomaly detection targets the detection and localization of defects in 3D point clouds trained solely on normal data. While a unified model improves scalability by learning acro…
cs.LG2025
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
Min-Yeong Park, Won-Jeong Lee, Seong Tae Kim +1
Recently, forecasting future abnormal events has emerged as an important scenario to tackle real-world necessities. However, the solution of predicting specific future time points…