2 citations · 5 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…
Video Diffusion Models are Strong Video Inpainter
Minhyeok Lee, Suhwan Cho, Chajin Shin +3
Propagation-based video inpainting using optical flow at the pixel or feature level has recently garnered significant attention. However, it has limitations such as the inaccuracy…
DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priors
Dogyoon Lee, Minhyeok Lee, Chajin Shin +1
Neural Radiance Field (NeRF) has exhibited outstanding three-dimensional (3D) reconstruction quality via the novel view synthesis from multi-view images and paired calibrated camer…
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…