20 citations · 22 across the 5 of their papers we have counts for
8 papers · 1 filter
DESSERT: Diffusion-based Event-driven Single-frame Synthesis via Residual Training
Jiyun Kong, Jun-Hyuk Kim, Jong-Seok Lee
Video frame prediction extrapolates future frames from previous frames, but suffers from prediction errors in dynamic scenes due to the lack of information about the next frame. Ev…
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…
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…
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…