246 citations · 491 across the 30 of their papers we have counts for
7 papers · 1 filter
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…
SeqHAND:RGB-Sequence-Based 3D Hand Pose and Shape Estimation
John Yang, Hyung Jin Chang, Seungeui Lee +1
3D hand pose estimation based on RGB images has been studied for a long time. Most of the studies, however, have performed frame-by-frame estimation based on independent static ima…
Implications of Human Irrationality for Reinforcement Learning
Haiyang Chen, Hyung Jin Chang, Andrew Howes
Recent work in the behavioural sciences has begun to overturn the long-held belief that human decision making is irrational, suboptimal and subject to biases. This turn to the rati…
Class-Attentive Diffusion Network for Semi-Supervised Classification
Jongin Lim, Daeho Um, Hyung Jin Chang +2
Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal…
G2L-Net: Global to Local Network for Real-time 6D Pose Estimation with Embedding Vector Features
Wei Chen, Xi Jia, Hyung Jin Chang +2
In this paper, we propose a novel real-time 6D object pose estimation framework, named G2L-Net. Our network operates on point clouds from RGB-D detection in a divide-and-conquer fa…
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
Jongwon Choi, Kwang Moo Yi, Jihoon Kim +5
Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard…
Self-Supervised Visual Learning by Variable Playback Speeds Prediction of a Video
Hyeon Cho, Taehoon Kim, Hyung Jin Chang +1
We propose a self-supervised visual learning method by predicting the variable playback speeds of a video. Without semantic labels, we learn the spatio-temporal visual representati…