4 citations · 10 across the 5 of their papers we have counts for
5 papers
Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration
Wonjeong Choi, Jungwuk Park, Dong-Jun Han +2
Research interests in the robustness of deep neural networks against domain shifts have been rapidly increasing in recent years. Most existing works, however, focus on improving th…
EvoFed: Leveraging Evolutionary Strategies for Communication-Efficient Federated Learning
Mohammad Mahdi Rahimi, Hasnain Irshad Bhatti, Younghyun Park +2
Federated Learning (FL) is a decentralized machine learning paradigm that enables collaborative model training across dispersed nodes without having to force individual nodes to sh…
Task-Adaptive Feature Transformer with Semantic Enrichment for Few-Shot Segmentation
Jun Seo, Young-Hyun Park, Sung Whan Yoon +1
Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has…
Task-Adaptive Feature Transformer for Few-Shot Segmentation
Jun Seo, Young-Hyun Park, Sung-Whan Yoon +1
Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has…
CAFENet: Class-Agnostic Few-Shot Edge Detection Network
Young-Hyun Park, Jun Seo, Jaekyun Moon
We tackle a novel few-shot learning challenge, which we call few-shot semantic edge detection, aiming to localize crisp boundaries of novel categories using only a few labeled samp…