49 citations · 87 across the 13 of their papers we have counts for
7 papers · 1 filter
Smoother Network Tuning and Interpolation for Continuous-level Image Processing
Hyeongmin Lee, Taeoh Kim, Hanbin Son +3
In Convolutional Neural Network (CNN) based image processing, most studies propose networks that are optimized to single-level (or single-objective); thus, they underperform on oth…
Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features
MyeongAh Cho, Taeoh Kim, Woo Jin Kim +2
In contemporary society, surveillance anomaly detection, i.e., spotting anomalous events such as crimes or accidents in surveillance videos, is a critical task. As anomalies occur…
Enhanced Standard Compatible Image Compression Framework based on Auxiliary Codec Networks
Hanbin Son, Taeoh Kim, Hyeongmin Lee +1
To enhance image compression performance, recent deep neural network-based research can be divided into three categories: a learnable codec, a postprocessing network, and a compact…
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…
Regularized Adaptation for Stable and Efficient Continuous-Level Learning on Image Processing Networks
Hyeongmin Lee, Taeoh Kim, Hanbin Son +3
In Convolutional Neural Network (CNN) based image processing, most of the studies propose networks that are optimized for a single-level (or a single-objective); thus, they underpe…
Relational Deep Feature Learning for Heterogeneous Face Recognition
MyeongAh Cho, Taeoh Kim, Ig-Jae Kim +2
Heterogeneous Face Recognition (HFR) is a task that matches faces across two different domains such as visible light (VIS), near-infrared (NIR), or the sketch domain. Due to the la…