2 citations · 3 across the 6 of their papers we have counts for
6 papers
Multi-Scale Feature Prediction with Auxiliary-Info for Neural Image Compression
Chajin Shin, Sangjin Lee, Sangyoun Lee
Recently, significant improvements in rate-distortion performance of image compression have been achieved with deep-learning techniques. A key factor in this success is the use of…
One-Shot Video Inpainting
Sangjin Lee, Suhwan Cho, Sangyoun Lee
Recently, removing objects from videos and filling in the erased regions using deep video inpainting (VI) algorithms has attracted considerable attention. Usually, a video sequence…
Expanded Adaptive Scaling Normalization for End to End Image Compression
Chajin Shin, Hyeongmin Lee, Hanbin Son +3
Recently, learning-based image compression methods that utilize convolutional neural layers have been developed rapidly. Rescaling modules such as batch normalization which are oft…
Exploring Discontinuity for Video Frame Interpolation
Sangjin Lee, Hyeongmin Lee, Chajin Shin +2
Video frame interpolation (VFI) is the task that synthesizes the intermediate frame given two consecutive frames. Most of the previous studies have focused on appropriate frame war…
Test-Time Adaptation for Out-of-distributed Image Inpainting
Chajin Shin, Taeoh Kim, Sangjin Lee +1
Deep learning-based image inpainting algorithms have shown great performance via powerful learned prior from the numerous external natural images. However, they show unpleasant res…
Extrapolative-Interpolative Cycle-Consistency Learning for Video Frame Extrapolation
Sangjin Lee, Hyeongmin Lee, Taeoh Kim +1
Video frame extrapolation is a task to predict future frames when the past frames are given. Unlike previous studies that usually have been focused on the design of modules or cons…