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20172021
most citedDynamics Transfer GAN: Generating Video by Transferring Arbitrary Temporal Dynamics from a Source Video to a Single Target Image

15 citations · 27 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.CV2018

Mode Variational LSTM Robust to Unseen Modes of Variation: Application to Facial Expression Recognition

Wissam J. Baddar, Yong Man Ro

Spatio-temporal feature encoding is essential for encoding the dynamics in video sequences. Recurrent neural networks, particularly long short-term memory (LSTM) units, have been p…

cs.CV2018

Measurement of exceptional motion in VR video contents for VR sickness assessment using deep convolutional autoencoder

Hak Gu Kim, Wissam J. Baddar, Heoun-taek Lim +2

This paper proposes a new objective metric of exceptional motion in VR video contents for VR sickness assessment. In VR environment, VR sickness can be caused by several factors wh…

cs.CV2017★ 10 cited

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…

cs.CV2017★ 15 cited

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…

cs.CV2017★ 1 cited

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…

cs.CV2017

Convolution with Logarithmic Filter Groups for Efficient Shallow CNN

Tae Kwan Lee, Wissam J. Baddar, Seong Tae Kim +1

In convolutional neural networks (CNNs), the filter grouping in convolution layers is known to be useful to reduce the network parameter size. In this paper, we propose a new logar…