3 citations · 3 across the 2 of their papers we have counts for
4 papers
Regularization Strategy for Point Cloud via Rigidly Mixed Sample
Dogyoon Lee, Jaeha Lee, Junhyeop Lee +4
Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks. However, data augmentation is rar…
Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition
Taeoh Kim, Hyeongmin Lee, MyeongAh Cho +3
Deep-Learning-based video recognition has shown promising improvements along with the development of large-scale datasets and spatiotemporal network architectures. In image recogni…
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…
AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation
Hyeongmin Lee, Taeoh Kim, Tae-young Chung +3
Video frame interpolation is one of the most challenging tasks in video processing research. Recently, many studies based on deep learning have been suggested. Most of these method…