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
14 papers · 2 filters
Relation Network for Person Re-identification
Hyunjong Park, Bumsub Ham
Person re-identification (reID) aims at retrieving an image of the person of interest from a set of images typically captured by multiple cameras. Recent reID methods have shown th…
GraphX-Convolution for Point Cloud Deformation in 2D-to-3D Conversion
Anh-Duc Nguyen, Seonghwa Choi, Woojae Kim +1
In this paper, we present a novel deep method to reconstruct a point cloud of an object from a single still image. Prior arts in the field struggle to reconstruct an accurate and s…
Learning Disentangled Representation for Robust Person Re-identification
Chanho Eom, Bumsub Ham
We address the problem of person re-identification (reID), that is, retrieving person images from a large dataset, given a query image of the person of interest. A key challenge is…
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction
Yunji Kim, Seonghyeon Nam, In Cho +1
We propose a deep video prediction model conditioned on a single image and an action class. To generate future frames, we first detect keypoints of a moving object and predict futu…
Deformable Kernel Networks for Joint Image Filtering
Beomjun Kim, Jean Ponce, Bumsub Ham
Joint image filters are used to transfer structural details from a guidance picture used as a prior to a target image, in tasks such as enhancing spatial resolution and suppressing…
On the Risk of Cancelable Biometrics
Xingbo Dong, Jaewoo Park, Zhe Jin +3
Cancelable biometrics (CB) employs an irreversible transformation to convert the biometric features into transformed templates while preserving the relative distance between two te…
Temporally Consistent Depth Prediction with Flow-Guided Memory Units
Chanho Eom, Hyunjong Park, Bumsub Ham
Predicting depth from a monocular video sequence is an important task for autonomous driving. Although it has advanced considerably in the past few years, recent methods based on c…
Universal Pooling -- A New Pooling Method for Convolutional Neural Networks
Junhyuk Hyun, Hongje Seong, Euntai Kim
Pooling is one of the main elements in convolutional neural networks. The pooling reduces the size of the feature map, enabling training and testing with a limited amount of comput…
FOSNet: An End-to-End Trainable Deep Neural Network for Scene Recognition
Hongje Seong, Junhyuk Hyun, Euntai Kim
Scene recognition is an image recognition problem aimed at predicting the category of the place at which the image is taken. In this paper, a new scene recognition method using the…
Fast User-Guided Video Object Segmentation by Interaction-and-Propagation Networks
Seoung Wug Oh, Joon-Young Lee, Ning Xu +1
We present a deep learning method for the interactive video object segmentation. Our method is built upon two core operations, interaction and propagation, and each operation is co…
A Genetic Algorithm Enabled Similarity-Based Attack on Cancellable Biometrics
Xingbo Dong, Zhe Jin, Andrew Teoh Beng Jin
Cancellable biometrics (CB) as a means for biometric template protection approach refers to an irreversible yet similarity preserving transformation on the original template. With…
An LBP-HOG Descriptor Based on Matrix Projection For Mammogram Classification
Zainab Alhakeem, Se-In Jang
In image based feature descriptor design, local information from image patches are extracted using iterative scanning operations which cause high computational costs. In order to a…
Deformable kernel networks for guided depth map upsampling
Beomjun Kim, Jean Ponce, Bumsub Ham
We address the problem of upsampling a low-resolution (LR) depth map using a registered high-resolution (HR) color image of the same scene. Previous methods based on convolutional…
Unpaired image denoising using a generative adversarial network in X-ray CT
Hyoung Suk Park, Jineon Baek, Sun Kyoung You +2
This paper proposes a deep learning-based denoising method for noisy low-dose computerized tomography (CT) images in the absence of paired training data. The proposed method uses a…