3 papers
cs.CV2026
Online Versatile Incremental Learning: Towards Class and Domain-Agnostic Adaptation at Any Time
Jae-Ho Lee, Min-Yeong Park, Jun-Yeong Moon +2
Continual learning enables vision systems to adapt to ever-changing data distributions. Despite significant advances, existing approaches fail to capture continuous and concurrent…
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…
cs.CV2024
Versatile Incremental Learning: Towards Class and Domain-Agnostic Incremental Learning
Min-Yeong Park, Jae-Ho Lee, Gyeong-Moon Park
Incremental Learning (IL) aims to accumulate knowledge from sequential input tasks while overcoming catastrophic forgetting. Existing IL methods typically assume that an incoming t…