22 citations · 95 across the 15 of their papers we have counts for
8 papers · 1 filter
Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data
Geonmo Gu, Seong Tae Kim, Kihyun Kim +2
In face-related applications with a public available dataset, synthesizing non-linear facial variations (e.g., facial expression, head-pose, illumination, etc.) through a generativ…
Dynamics Transfer GAN: Generating Video by Transferring Arbitrary Temporal Dynamics from a Source Video to a Single Target Image
Wissam J. Baddar, Geonmo Gu, Sangmin Lee +1
In this paper, we propose Dynamics Transfer GAN; a new method for generating video sequences based on generative adversarial learning. The spatial constructs of a generated video s…
Learning Spatio-temporal Features with Partial Expression Sequences for on-the-Fly Prediction
Wissam J. Baddar, Yong Man Ro
Spatio-temporal feature encoding is essential for encoding facial expression dynamics in video sequences. At test time, most spatio-temporal encoding methods assume that a temporal…
Facial Dynamics Interpreter Network: What are the Important Relations between Local Dynamics for Facial Trait Estimation?
Seong Tae Kim, Yong Man Ro
Human face analysis is an important task in computer vision. According to cognitive-psychological studies, facial dynamics could provide crucial cues for face analysis. The motion…
Iterative Deep Convolutional Encoder-Decoder Network for Medical Image Segmentation
Jung Uk Kim, Hak Gu Kim, Yong Man Ro
In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network…
Modality-bridge Transfer Learning for Medical Image Classification
Hak Gu Kim, Yeoreum Choi, Yong Man Ro
This paper presents a new approach of transfer learning-based medical image classification to mitigate insufficient labeled data problem in medical domain. Instead of direct transf…