49 citations · 158 across the 43 of their papers we have counts for
46 papers
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…
Adaptive Graph Convolution Module for Salient Object Detection
Yongwoo Lee, Minhyeok Lee, Suhwan Cho +1
Salient object detection (SOD) is a task that involves identifying and segmenting the most visually prominent object in an image. Existing solutions can accomplish this use a multi…
Guided Slot Attention for Unsupervised Video Object Segmentation
Minhyeok Lee, Suhwan Cho, Dogyoon Lee +3
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence. However, the existence of complex backgrounds and multiple foreground objects…
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation
Seunghoon Lee, Suhwan Cho, Dogyoon Lee +2
Unsupervised Video Object Segmentation (UVOS) refers to the challenging task of segmenting the prominent object in videos without manual guidance. In recent works, two approaches f…
One-Shot Video Inpainting
Sangjin Lee, Suhwan Cho, Sangyoun Lee
Recently, removing objects from videos and filling in the erased regions using deep video inpainting (VI) algorithms has attracted considerable attention. Usually, a video sequence…
Two-stream Decoder Feature Normality Estimating Network for Industrial Anomaly Detection
Chaewon Park, Minhyeok Lee, Suhwan Cho +2
Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets. These approaches work by learning to mod…
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…
Leveraging Spatio-Temporal Dependency for Skeleton-Based Action Recognition
Jungho Lee, Minhyeok Lee, Suhwan Cho +3
Skeleton-based action recognition has attracted considerable attention due to its compact representation of the human body's skeletal sructure. Many recent methods have achieved re…
Treating Motion as Option to Reduce Motion Dependency in Unsupervised Video Object Segmentation
Suhwan Cho, Minhyeok Lee, Seunghoon Lee +3
Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level. In unsupervised VOS, most state-of-the-art methods lever…
Boundary-aware Camouflaged Object Detection via Deformable Point Sampling
Minhyeok Lee, Suhwan Cho, Chaewon Park +3
The camouflaged object detection (COD) task aims to identify and segment objects that blend into the background due to their similar color or texture. Despite the inherent difficul…
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…
Dual Prototype Attention for Unsupervised Video Object Segmentation
Suhwan Cho, Minhyeok Lee, Seunghoon Lee +4
Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos. The primary techniques used in unsupervised VOS are 1) the collaboration…
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly Detection
Donghyeong Kim, Chaewon Park, Suhwan Cho +1
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images. However, some method…
Unsupervised Video Object Segmentation via Prototype Memory Network
Minhyeok Lee, Suhwan Cho, Seunghoon Lee +2
Unsupervised video object segmentation aims to segment a target object in the video without a ground truth mask in the initial frame. This challenging task requires extracting feat…
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…
Tackling Background Distraction in Video Object Segmentation
Suhwan Cho, Heansung Lee, Minhyeok Lee +4
Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos. One of the main challenges in this task is the existence of background d…
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…
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…
SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection
Minhyeok Lee, Chaewon Park, Suhwan Cho +1
RGB-D salient object detection (SOD) has been in the spotlight recently because it is an important preprocessing operation for various vision tasks. However, despite advances in de…
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…
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…
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…