22 citations · 110 across the 22 of their papers we have counts for
26 papers
Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck
Junho Kim, Byung-Kwan Lee, Yong Man Ro
Adversarial examples, generated by carefully crafted perturbation, have attracted considerable attention in research fields. Recent works have argued that the existence of the robu…
Distinguishing Homophenes Using Multi-Head Visual-Audio Memory for Lip Reading
Minsu Kim, Jeong Hun Yeo, Yong Man Ro
Recognizing speech from silent lip movement, which is called lip reading, is a challenging task due to 1) the inherent information insufficiency of lip movement to fully represent…
Multi-modality Associative Bridging through Memory: Speech Sound Recollected from Face Video
Minsu Kim, Joanna Hong, Se Jin Park +1
In this paper, we introduce a novel audio-visual multi-modal bridging framework that can utilize both audio and visual information, even with uni-modal inputs. We exploit a memory…
Defending Person Detection Against Adversarial Patch Attack by using Universal Defensive Frame
Youngjoon Yu, Hong Joo Lee, Hakmin Lee +1
Person detection has attracted great attention in the computer vision area and is an imperative element in human-centric computer vision. Although the predictive performances of pe…
Video Prediction Recalling Long-term Motion Context via Memory Alignment Learning
Sangmin Lee, Hak Gu Kim, Dae Hwi Choi +2
Our work addresses long-term motion context issues for predicting future frames. To predict the future precisely, it is required to capture which long-term motion context (e.g., wa…
Comprehensive Facial Expression Synthesis using Human-Interpretable Language
Joanna Hong, Jung Uk Kim, Sangmin Lee +1
Recent advances in facial expression synthesis have shown promising results using diverse expression representations including facial action units. Facial action units for an elabo…
Investigating Vulnerability to Adversarial Examples on Multimodal Data Fusion in Deep Learning
Youngjoon Yu, Hong Joo Lee, Byeong Cheon Kim +2
The success of multimodal data fusion in deep learning appears to be attributed to the use of complementary in-formation between multiple input data. Compared to their predictive p…
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
Hong Joo Lee, Seong Tae Kim, Hakmin Lee +2
Recent studies have shown that ensemble approaches could not only improve accuracy and but also estimate model uncertainty in deep learning. However, it requires a large number of…
Revisiting Role of Autoencoders in Adversarial Settings
Byeong Cheon Kim, Jung Uk Kim, Hakmin Lee +1
To combat against adversarial attacks, autoencoder structure is widely used to perform denoising which is regarded as gradient masking. In this paper, we revisit the role of autoen…
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
Hakmin Lee, Hong Joo Lee, Seong Tae Kim +1
Deep neural networks have achieved substantial achievements in several computer vision areas, but have vulnerabilities that are often fooled by adversarial examples that are not re…
Generative Guiding Block: Synthesizing Realistic Looking Variants Capable of Even Large Change Demands
Minho Park, Hak Gu Kim, Yong Man Ro
Realistic image synthesis is to generate an image that is perceptually indistinguishable from an actual image. Generating realistic looking images with large variations (e.g., larg…
Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis
Hyebin Lee, Seong Tae Kim, Yong Man Ro
The ambiguity of the decision-making process has been pointed out as the main obstacle to applying the deep learning-based method in a practical way in spite of its outstanding per…
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…
Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation
Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee +1
This paper deals with a method for generating realistic labeled masses. Recently, there have been many attempts to apply deep learning to various bio-image computing fields includi…
ICADx: Interpretable computer aided diagnosis of breast masses
Seong Tae Kim, Hakmin Lee, Hak Gu Kim +1
In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has…
STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection
Sangmin Lee, Hak Gu Kim, Yong Man Ro
In this paper, we propose a novel abnormal event detection method with spatio-temporal adversarial networks (STAN). We devise a spatio-temporal generator which synthesizes an inter…
VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning
Heoun-taek Lim, Hak Gu Kim, Yong Man Ro
In this paper, we propose a novel virtual reality image quality assessment (VR IQA) with adversarial learning for omnidirectional images. To take into account the characteristics o…
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…
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…