49 citations · 87 across the 13 of their papers we have counts for
14 papers
Masked Autoencoder for Unsupervised Video Summarization
Minho Shim, Taeoh Kim, Jinhyung Kim +1
Summarizing a video requires a diverse understanding of the video, ranging from recognizing scenes to evaluating how much each frame is essential enough to be selected as a summary…
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection
Pilhyeon Lee, Taeoh Kim, Minho Shim +2
Temporal action detection aims to predict the time intervals and the classes of action instances in the video. Despite the promising performance, existing two-stream models exhibit…
NIR-to-VIS Face Recognition via Embedding Relations and Coordinates of the Pairwise Features
MyeongAh Cho, Tae-young Chun, g Taeoh Kim +1
NIR-to-VIS face recognition is identifying faces of two different domains by extracting domain-invariant features. However, this is a challenging problem due to the two different d…
Exploring Temporally Dynamic Data Augmentation for Video Recognition
Taeoh Kim, Jinhyung Kim, Minho Shim +4
Data augmentation has recently emerged as an essential component of modern training recipes for visual recognition tasks. However, data augmentation for video recognition has been…
Frequency Selective Augmentation for Video Representation Learning
Jinhyung Kim, Taeoh Kim, Minho Shim +3
Recent self-supervised video representation learning methods focus on maximizing the similarity between multiple augmented views from the same video and largely rely on the quality…
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…
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…
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…