activity
20192025
most citedKernelized Memory Network for Video Object Segmentation

11 citations · 20 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CV2025

Environmental Change Detection: Toward a Practical Task of Scene Change Detection

Kyusik Cho, Suhan Woo, Hongje Seong +1

Humans do not memorize everything. Thus, humans recognize scene changes by exploring the past images. However, available past (i.e., reference) images typically represent nearby vi…

cs.CV2022

Correlation Verification for Image Retrieval

Seongwon Lee, Hongje Seong, Suhyeon Lee +1

Geometric verification is considered a de facto solution for the re-ranking task in image retrieval. In this study, we propose a novel image retrieval re-ranking network named Corr…

cs.CV20224 cited

WildNet: Learning Domain Generalized Semantic Segmentation from the Wild

Suhyeon Lee, Hongje Seong, Seongwon Lee +1

We present a new domain generalized semantic segmentation network named WildNet, which learns domain-generalized features by leveraging a variety of contents and styles from the wi…

cs.CV2021

Hierarchical Memory Matching Network for Video Object Segmentation

Hongje Seong, Seoung Wug Oh, Joon-Young Lee +3

We present Hierarchical Memory Matching Network (HMMN) for semi-supervised video object segmentation. Based on a recent memory-based method [33], we propose two advanced memory rea…

cs.CV20204 cited

Unsupervised Domain Adaptation for Semantic Segmentation by Content Transfer

Suhyeon Lee, Junhyuk Hyun, Hongje Seong +1

In this paper, we tackle the unsupervised domain adaptation (UDA) for semantic segmentation, which aims to segment the unlabeled real data using labeled synthetic data. The main pr…

cs.CV202011 cited

Kernelized Memory Network for Video Object Segmentation

Hongje Seong, Junhyuk Hyun, Euntai Kim

Semi-supervised video object segmentation (VOS) is a task that involves predicting a target object in a video when the ground truth segmentation mask of the target object is given…