3 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CV2024
Is user feedback always informative? Retrieval Latent Defending for Semi-Supervised Domain Adaptation without Source Data
Junha Song, Tae Soo Kim, Junha Kim +3
This paper aims to adapt the source model to the target environment, leveraging small user feedback (i.e., labeled target data) readily available in real-world applications. We fin…
cs.CV2023★ 3 cited
Enhancing Breast Cancer Risk Prediction by Incorporating Prior Images
Hyeonsoo Lee, Junha Kim, Eunkyung Park +3
Recently, deep learning models have shown the potential to predict breast cancer risk and enable targeted screening strategies, but current models do not consider the change in the…
cs.CV2022
Learning to Adapt to Unseen Abnormal Activities under Weak Supervision
Jaeyoo Park, Junha Kim, Bohyung Han
We present a meta-learning framework for weakly supervised anomaly detection in videos, where the detector learns to adapt to unseen types of abnormal activities effectively when o…