4 citations · 9 across the 10 of their papers we have counts for
8 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…
High-Quality Unknown Object Instance Segmentation via Quadruple Boundary Error Refinement
Seunghyeok Back, Sangbeom Lee, Kangmin Kim +4
Accurate and efficient segmentation of unknown objects in unstructured environments is essential for robotic manipulation. Unknown Object Instance Segmentation (UOIS), which aims t…
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…
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…
Automatic Detection of Injection and Press Mold Parts on 2D Drawing Using Deep Neural Network
Junseok Lee, Jongwon Kim, Jumi Park +3
This paper proposes a method to automatically detect the key feature parts in a CAD of commercial TV and monitor using a deep neural network. We developed a deep learning pipeline…