4 citations · 6 across the 5 of their papers we have counts for
7 papers
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…
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…
Deep Learning based Food Instance Segmentation using Synthetic Data
D. Park, J. Lee, K. Lee
In the process of intelligently segmenting foods in images using deep neural networks for diet management, data collection and labeling for network training are very important but…
Multiple Classification with Split Learning
Jongwon Kim, Sungho Shin, Yeonguk Yu +2
Privacy issues were raised in the process of training deep learning in medical, mobility, and other fields. To solve this problem, we present privacy-preserving distributed deep le…
Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data
Seunghyeok Back, Jongwon Kim, Raeyoung Kang +2
Segmentation of unseen industrial parts is essential for autonomous industrial systems. However, industrial components are texture-less, reflective, and often found in cluttered an…