49 citations · 63 across the 15 of their papers we have counts for
15 papers · 1 filter
Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation
Suhwan Cho, Minhyeok Lee, Jungho Lee +5
Unsupervised video object segmentation aims to detect the most salient object in a video without any external guidance regarding the object. Salient objects often exhibit distincti…
Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification
Minjung Kim, MyeongAh Cho, Sangyoun Lee
In video person re-identification (Re-ID), the network must consistently extract features of the target person from successive frames. Existing methods tend to focus only on how to…
Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning
Minjung Kim, MyeongAh Cho, Heansung Lee +2
Occluded person re-identification (Re-ID) in images captured by multiple cameras is challenging because the target person is occluded by pedestrians or objects, especially in crowd…
Pixel-Level Equalized Matching for Video Object Segmentation
Suhwan Cho, Woo Jin Kim, MyeongAh Cho +4
Feature similarity matching, which transfers the information of the reference frame to the query frame, is a key component in semi-supervised video object segmentation. If surjecti…
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…
N-RPN: Hard Example Learning for Region Proposal Networks
MyeongAh Cho, Tae-young Chung, Hyeongmin Lee +1
The region proposal task is to generate a set of candidate regions that contain an object. In this task, it is most important to propose as many candidates of ground-truth as possi…
RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection
Chaewon Park, Minhyeok Lee, MyeongAh Cho +1
Recent anomaly detection algorithms have shown powerful performance by adopting frame predicting autoencoders. However, these methods face two challenging circumstances. First, the…
Saliency Detection via Global Context Enhanced Feature Fusion and Edge Weighted Loss
Chaewon Park, Minhyeok Lee, MyeongAh Cho +1
UNet-based methods have shown outstanding performance in salient object detection (SOD), but are problematic in two aspects. 1) Indiscriminately integrating the encoder feature, wh…
FastAno: Fast Anomaly Detection via Spatio-temporal Patch Transformation
Chaewon Park, MyeongAh Cho, Minhyeok Lee +1
Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos. Especially, the prediction based approa…
A NIR-to-VIS face recognition via part adaptive and relation attention module
Rushuang Xu, MyeongAh Cho, Sangyoun Lee
In the face recognition application scenario, we need to process facial images captured in various conditions, such as at night by near-infrared (NIR) surveillance cameras. The ill…
Multi-object tracking with self-supervised associating network
Tae-young Chung, Heansung Lee, Myeong Ah Cho +2
Multi-Object Tracking (MOT) is the task that has a lot of potential for development, and there are still many problems to be solved. In the traditional tracking by detection paradi…
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…
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…
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…
CRVOS: Clue Refining Network for Video Object Segmentation
Suhwan Cho, MyeongAh Cho, Tae-young Chung +2
The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances. However, most of th…