15 citations · 27 across the 4 of their papers we have counts for
7 papers
Self-Reorganizing and Rejuvenating CNNs for Increasing Model Capacity Utilization
Wissam J. Baddar, Seungju Han, Seonmin Rhee +1
In this paper, we propose self-reorganizing and rejuvenating convolutional neural networks; a biologically inspired method for improving the computational resource utilization of n…
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…
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…
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…