67 citations · 235 across the 15 of their papers we have counts for
7 papers · 1 filter
Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
Seokeon Choi, Sumin Lee, Youngeun Kim +2
Visible-infrared person re-identification (VI-ReID) is an important task in night-time surveillance applications, since visible cameras are difficult to capture valid appearance in…
RPM-Net: Robust Pixel-Level Matching Networks for Self-Supervised Video Object Segmentation
Youngeun Kim, Seokeon Choi, Hankyeol Lee +2
In this paper, we introduce a self-supervised approach for video object segmentation without human labeled data.Specifically, we present Robust Pixel-level Matching Net-works (RPM-…
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…
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…
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-…