148 citations · 397 across the 22 of their papers we have counts for
23 papers
Joint Global and Local Hierarchical Priors for Learned Image Compression
Jun-Hyuk Kim, Byeongho Heo, Jong-Seok Lee
Recently, learned image compression methods have outperformed traditional hand-crafted ones including BPG. One of the keys to this success is learned entropy models that estimate t…
Light Lies: Optical Adversarial Attack
Kyulim Kim, JeongSoo Kim, Seungri Song +3
A significant amount of work has been done on adversarial attacks that inject imperceptible noise to images to deteriorate the image classification performance of deep models. Howe…
Deep Image Destruction: Vulnerability of Deep Image-to-Image Models against Adversarial Attacks
Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim +2
Recently, the vulnerability of deep image classification models to adversarial attacks has been investigated. However, such an issue has not been thoroughly studied for image-to-im…
Emotional EEG Classification using Connectivity Features and Convolutional Neural Networks
Seong-Eun Moon, Chun-Jui Chen, Cho-Jui Hsieh +2
Convolutional neural networks (CNNs) are widely used to recognize the user's state through electroencephalography (EEG) signals. In the previous studies, the EEG signals are usuall…
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack
Jaehui Hwang, Jun-Hyuk Kim, Jun-Ho Choi +1
The video-based action recognition task has been extensively studied in recent years. In this paper, we study the structural vulnerability of deep learning-based action recognition…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…