4.3k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Seoul National UniversityKR1.2k papers
- The University of TokyoJP1.2k papers
- Panjab UniversityIN1.2k papers
- Centre National de la Recherche ScientifiqueFR1.1k papers
- Sungkyunkwan UniversityKR1.1k papers
- Wayne State UniversityUS990 papers
- Korea UniversityKR986 papers
- National Taiwan UniversityTW970 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di TorinoIT941 papers
- Kyungpook National UniversityKR930 papers
- University of Science and Technology of ChinaCN930 papers
- University of Tennessee at KnoxvilleUS920 papers
24 papers · 2 filters
Unsupervised Domain Adaptation for Semantic Segmentation by Content Transfer
Suhyeon Lee, Junhyuk Hyun, Hongje Seong +1
In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment the unlabeled real data using labeled synthetic data. The main pr…
Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation
Minsu Kim, Sunghun Joung, Seungryong Kim +3
Existing techniques to adapt semantic segmentation networks across the source and target domains within deep convolutional neural networks (CNNs) deal with all the samples from the…
Automated 3D cephalometric landmark identification using computerized tomography
Hye Sun Yun, Chang Min Hyun, Seong Hyeon Baek +2
Identification of 3D cephalometric landmarks that serve as proxy to the shape of human skull is the fundamental step in cephalometric analysis. Since manual landmarking from 3D com…
FairFaceGAN: Fairness-aware Facial Image-to-Image Translation
Sunhee Hwang, Sungho Park, Dohyung Kim +2
In this paper, we introduce FairFaceGAN, a fairness-aware facial Image-to-Image translation model, mitigating the problem of unwanted translation in protected attributes (e.g., gen…
Periocular Embedding Learning with Consistent Knowledge Distillation from Face
Yoon Gyo Jung, Jaewoo Park, Cheng Yaw Low +3
Periocular biometric, the peripheral area of the ocular, is a collaborative alternative to the face, especially when the face is occluded or masked. However, in practice, sole peri…
PMVOS: Pixel-Level Matching-Based Video Object Segmentation
Suhwan Cho, Heansung Lee, Sungmin Woo +2
Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided. Due to t…
Adaptive confidence thresholding for monocular depth estimation
Hyesong Choi, Hunsang Lee, Sunkyung Kim +4
Self-supervised monocular depth estimation has become an appealing solution to the lack of ground truth labels, but its reconstruction loss often produces over-smoothed results acr…
3D-DEEP: 3-Dimensional Deep-learning based on elevation patterns forroad scene interpretation
A. Hernández, S. Woo, H. Corrales +4
Road detection and segmentation is a crucial task in computer vision for safe autonomous driving. With this in mind, a new net architecture (3D-DEEP) and its end-to-end training me…
Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition
Taeoh Kim, Hyeongmin Lee, MyeongAh Cho +3
Deep-Learning-based video recognition has shown promising improvements along with the development of large-scale datasets and spatiotemporal network architectures. In image recogni…
Learning to Caricature via Semantic Shape Transform
Wenqing Chu, Wei-Chih Hung, Yi-Hsuan Tsai +4
Caricature is an artistic drawing created to abstract or exaggerate facial features of a person. Rendering visually pleasing caricatures is a difficult task that requires professio…
Cross-Identity Motion Transfer for Arbitrary Objects through Pose-Attentive Video Reassembling
Subin Jeon, Seonghyeon Nam, Seoung Wug Oh +1
We propose an attention-based networks for transferring motions between arbitrary objects. Given a source image(s) and a driving video, our networks animate the subject in the sour…
Kernelized Memory Network for Video Object Segmentation
Hongje Seong, Junhyuk Hyun, Euntai Kim
Semi-supervised video object segmentation (VOS) is a task that involves predicting a target object in a video when the ground truth segmentation mask of the target object is given…
Learning with Privileged Information for Efficient Image Super-Resolution
Wonkyung Lee, Junghyup Lee, Dohyung Kim +1
Convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR) over the last decade. Most SR methods based on CNNs have focused on ac…
Rethinking the Truly Unsupervised Image-to-Image Translation
Kyungjune Baek, Yunjey Choi, Youngjung Uh +2
Every recent image-to-image translation model inherently requires either image-level (i.e. input-output pairs) or set-level (i.e. domain labels) supervision. However, even set-leve…
False Positive Removal for 3D Vehicle Detection with Penetrated Point Classifier
Sungmin Woo, Sangwon Hwang, Woojin Kim +3
Recently, researchers have been leveraging LiDAR point cloud for higher accuracy in 3D vehicle detection. Most state-of-the-art methods are deep learning based, but are easily affe…
One-vs-Rest Network-based Deep Probability Model for Open Set Recognition
Jaeyeon Jang, Chang Ouk Kim
Unknown examples that are unseen during training often appear in real-world computer vision tasks, and an intelligent self-learning system should be able to differentiate between k…
Extrapolative-Interpolative Cycle-Consistency Learning for Video Frame Extrapolation
Sangjin Lee, Hyeongmin Lee, Taeoh Kim +1
Video frame extrapolation is a task to predict future frames when the past frames are given. Unlike previous studies that usually have been focused on the design of modules or cons…
Deep Space-Time Video Upsampling Networks
Jaeyeon Kang, Younghyun Jo, Seoung Wug Oh +2
Video super-resolution (VSR) and frame interpolation (FI) are traditional computer vision problems, and the performance have been improving by incorporating deep learning recently.…
Learning Memory-guided Normality for Anomaly Detection
Hyunjong Park, Jongyoun Noh, Bumsub Ham
We address the problem of anomaly detection, that is, detecting anomalous events in a video sequence. Anomaly detection methods based on convolutional neural networks (CNNs) typica…
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
Myeongjin Kim, Hyeran Byun
Since annotating pixel-level labels for semantic segmentation is laborious, leveraging synthetic data is an attractive solution. However, due to the domain gap between synthetic do…
Relational Deep Feature Learning for Heterogeneous Face Recognition
MyeongAh Cho, Taeoh Kim, Ig-Jae Kim +2
Heterogeneous Face Recognition (HFR) is a task that matches faces across two different domains such as visible light (VIS), near-infrared (NIR), or the sketch domain. Due to the la…
CRVOS: Clue Refining Network for Video Object Segmentation
Suhwan Cho, MyeongAh Cho, Tae-young Chung +2
The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances. However, most of th…
AD-VO: Scale-Resilient Visual Odometry Using Attentive Disparity Map
Joosung Lee, Sangwon Hwang, Kyungjae Lee +4
Visual odometry is an essential key for a localization module in SLAM systems. However, previous methods require tuning the system to adapt environment changes. In this paper, we p…
Evaluating Weakly Supervised Object Localization Methods Right
Junsuk Choe, Seong Joon Oh, Seungho Lee +3
Weakly-supervised object localization (WSOL) has gained popularity over the last years for its promise to train localization models with only image-level labels. Since the seminal…