activity
20172022
most citedMAGAN: Margin Adaptation for Generative Adversarial Networks

54 citations · 93 across the 8 of their papers we have counts for

collaborators

16 papers

cs.CV202218 cited

Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution

Tze Ho Elden Tse, Kwang In Kim, Ales Leonardis +1

Estimating the pose and shape of hands and objects under interaction finds numerous applications including augmented and virtual reality. Existing approaches for hand and object re…

cs.CV202217 cited

Repurposing Existing Deep Networks for Caption and Aesthetic-Guided Image Cropping

Nora Horanyi, Kedi Xia, Kwang Moo Yi +3

We propose a novel optimization framework that crops a given image based on user description and aesthetics. Unlike existing image cropping methods, where one typically trains a de…

cs.CV2021

Nesterov Accelerated ADMM for Fast Diffeomorphic Image Registration

Alexander Thorley, Xi Jia, Hyung Jin Chang +8

Deterministic approaches using iterative optimisation have been historically successful in diffeomorphic image registration (DiffIR). Although these approaches are highly accurate,…

cs.LG2021

Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders

Jiwoong Park, Junho Cho, Hyung Jin Chang +1

Most of the existing literature regarding hyperbolic embedding concentrate upon supervised learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In…

cs.CV2021

FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism

Wei Chen, Xi Jia, Hyung Jin Chang +3

In this paper, we focus on category-level 6D pose and size estimation from monocular RGB-D image. Previous methods suffer from inefficient category-level pose feature extraction wh…

cs.LG2020

Combining Task Predictors via Enhancing Joint Predictability

Kwang In Kim, Christian Richardt, Hyung Jin Chang

Predictor combination aims to improve a (target) predictor of a learning task based on the (reference) predictors of potentially relevant tasks, without having access to the intern…