53 citations · 53 across the 3 of their papers we have counts for
7 papers
SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments
Jaehoon Choi, Dongki Jung, Yonghan Lee +3
We present a novel algorithm for self-supervised monocular depth completion. Our approach is based on training a neural network that requires only sparse depth measurements and cor…
Arbitrary Style Transfer using Graph Instance Normalization
Dongki Jung, Seunghan Yang, Jaehoon Choi +1
Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content. In statistical methods, the adaptive instance normalization…
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
Jaehoon Choi, Dongki Jung, Donghwan Lee +1
Self-supervised monocular depth estimation has emerged as a promising method because it does not require groundtruth depth maps during training. As an alternative for the groundtru…
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…
Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation
Jaehoon Choi, Taekyung Kim, Changick Kim
Deep learning-based semantic segmentation methods have an intrinsic limitation that training a model requires a large amount of data with pixel-level annotations. To address this c…
Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation
Jaehoon Choi, Minki Jeong, Taekyung Kim +1
To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation. However, the existence of false pseudo-la…