70 citations · 84 across the 9 of their papers we have counts for
5 papers · 1 filter
Curriculum Fine-tuning of Vision Foundation Model for Medical Image Classification Under Label Noise
Yeonguk Yu, Minhwan Ko, Sungho Shin +2
Deep neural networks have demonstrated remarkable performance in various vision tasks, but their success heavily depends on the quality of the training data. Noisy labels are a cri…
Domain-Specific Block Selection and Paired-View Pseudo-Labeling for Online Test-Time Adaptation
Yeonguk Yu, Sungho Shin, Seunghyeok Back +3
Test-time adaptation (TTA) aims to adapt a pre-trained model to a new test domain without access to source data after deployment. Existing approaches typically rely on self-trainin…
Enhancing Low-resolution Face Recognition with Feature Similarity Knowledge Distillation
Sungho Shin, Yeonguk Yu, Kyoobin Lee
In this study, we introduce a feature knowledge distillation framework to improve low-resolution (LR) face recognition performance using knowledge obtained from high-resolution (HR…
Block Selection Method for Using Feature Norm in Out-of-distribution Detection
Yeonguk Yu, Sungho Shin, Seongju Lee +2
Detecting out-of-distribution (OOD) inputs during the inference stage is crucial for deploying neural networks in the real world. Previous methods commonly relied on the output of…
Teaching Where to Look: Attention Similarity Knowledge Distillation for Low Resolution Face Recognition
Sungho Shin, Joosoon Lee, Junseok Lee +2
Deep learning has achieved outstanding performance for face recognition benchmarks, but performance reduces significantly for low resolution (LR) images. We propose an attention si…