32 citations · 91 across the 5 of their papers we have counts for
5 papers
Significantly improving zero-shot X-ray pathology classification via fine-tuning pre-trained image-text encoders
Jongseong Jang, Daeun Kyung, Seung Hwan Kim +3
Deep neural networks are increasingly used in medical imaging for tasks such as pathological classification, but they face challenges due to the scarcity of high-quality, expert-la…
Enriched CNN-Transformer Feature Aggregation Networks for Super-Resolution
Jinsu Yoo, Taehoon Kim, Sihaeng Lee +3
Recent transformer-based super-resolution (SR) methods have achieved promising results against conventional CNN-based methods. However, these approaches suffer from essential short…
CNN-based Semantic Segmentation using Level Set Loss
Youngeun Kim, Seunghyeon Kim, Taekyung Kim +1
Thesedays, Convolutional Neural Networks are widely used in semantic segmentation. However, since CNN-based segmentation networks produce low-resolution outputs with rich semantic…
Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection
Seunghyeon Kim, Jaehoon Choi, Taekyung Kim +1
Deep learning-based object detectors have shown remarkable improvements. However, supervised learning-based methods perform poorly when the train data and the test data have differ…
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection
Taekyung Kim, Minki Jeong, Seunghyeon Kim +2
We introduce a novel unsupervised domain adaptation approach for object detection. We aim to alleviate the imperfect translation problem of pixel-level adaptations, and the source-…