20 citations · 46 across the 13 of their papers we have counts for
15 papers
Coarse-to-Fine: Progressive Image Compression for Semantically Hierarchical Classification
Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok Lee
Recent advances in learned image compression (LIC) have enabled practical deployments, spurring active research into image compression for machines and progressive coding schemes.…
SLIM: Semantic-based Low-bitrate Image compression for Machines
Hyeonjin Lee, Jun-Hyuk Kim, Jong-Seok Lee
In recent years, the demand of image compression models for machine vision has increased dramatically. However, the training frameworks of image compression still focus on the visi…
Event-based Scene Synthesis via Inter-Frame Residual Alignment
Jiyun Kong, Jun-Hyuk Kim, Jong-Seok Lee
Event-based scene synthesis reconstructs target RGB frames from sparse image observations and asynchronous event streams, encompassing both video frame prediction and interpolation…
Progressive Learned Image Compression for Machine Perception
Jungwoo Kim, Jun-Hyuk Kim, Jong-Seok Lee
Recent advances in learned image codecs have extended from human perception toward machine perception However, progressive image compression with fine granular scalability (FGS)-wh…
Demystifying Randomly Initialized Networks for Evaluating Generative Models
Junghyuk Lee, Jun-Hyuk Kim, Jong-Seok Lee
Evaluation of generative models is mostly based on the comparison between the estimated distribution and the ground truth distribution in a certain feature space. To embed samples…
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…
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…
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…
AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results
Kai Zhang, Martin Danelljan, Yawei Li +75
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an in…
SRZoo: An integrated repository for super-resolution using deep learning
Jun-Ho Choi, Jun-Hyuk Kim, Jong-Seok Lee
Deep learning-based image processing algorithms, including image super-resolution methods, have been proposed with significant improvement in performance in recent years. However,…
Evaluating Robustness of Deep Image Super-Resolution against Adversarial Attacks
Jun-Ho Choi, Huan Zhang, Jun-Hyuk Kim +2
Single-image super-resolution aims to generate a high-resolution version of a low-resolution image, which serves as an essential component in many computer vision applications. Thi…
MAMNet: Multi-path Adaptive Modulation Network for Image Super-Resolution
Jun-Hyuk Kim, Jun-Ho Choi, Manri Cheon +1
In recent years, single image super-resolution (SR) methods based on deep convolutional neural networks (CNNs) have made significant progress. However, due to the non-adaptive natu…
Generative adversarial network-based image super-resolution using perceptual content losses
Manri Cheon, Jun-Hyuk Kim, Jun-Ho Choi +1
In this paper, we propose a deep generative adversarial network for super-resolution considering the trade-off between perception and distortion. Based on good performance of a rec…
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual Quality
Jun-Ho Choi, Jun-Hyuk Kim, Manri Cheon +1
Recently, it has been shown that in super-resolution, there exists a tradeoff relationship between the quantitative and perceptual quality of super-resolved images, which correspon…