11 citations · 23 across the 13 of their papers we have counts for
13 papers
ViewSplat: View-Adaptive 3D Gaussian Splatting for Feed-Forward Synthesis
Moonyeon Jeong, Seunggi Min, Suhyeon Lee +1
We present ViewSplat, a view-adaptive 3D Gaussian splatting network for novel view synthesis from unposed images. While recent feed-forward 3D Gaussian splatting has significantly…
Environmental Change Detection for Real-World Change Analysis
Kyusik Cho, Suhan Woo, Hongje Seong +1
Scene Change Detection (SCD) evaluates changes using predefined query-reference (i.e., present-past) image pairs. However, this formulation overlooks a critical dependency: the cor…
SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation
Seokbeom Song, Suhyeon Lee, Hongje Seong +2
We propose a novel solution for unpaired image-to-image (I2I) translation. To translate complex images with a wide range of objects to a different domain, recent approaches often u…
Domain Adaptive Video Semantic Segmentation via Cross-Domain Moving Object Mixing
Kyusik Cho, Suhyeon Lee, Hongje Seong +1
The network trained for domain adaptation is prone to bias toward the easy-to-transfer classes. Since the ground truth label on the target domain is unavailable during training, th…
One-Trimap Video Matting
Hongje Seong, Seoung Wug Oh, Brian Price +2
Recent studies made great progress in video matting by extending the success of trimap-based image matting to the video domain. In this paper, we push this task toward a more pract…
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…
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…
Iteratively Selecting an Easy Reference Frame Makes Unsupervised Video Object Segmentation Easier
Youngjo Lee, Hongje Seong, Euntai Kim
Unsupervised video object segmentation (UVOS) is a per-pixel binary labeling problem which aims at separating the foreground object from the background in the video without using t…
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…
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…
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…
Universal Pooling -- A New Pooling Method for Convolutional Neural Networks
Junhyuk Hyun, Hongje Seong, Euntai Kim
Pooling is one of the main elements in convolutional neural networks. The pooling reduces the size of the feature map, enabling training and testing with a limited amount of comput…
FOSNet: An End-to-End Trainable Deep Neural Network for Scene Recognition
Hongje Seong, Junhyuk Hyun, Euntai Kim
Scene recognition is an image recognition problem aimed at predicting the category of the place at which the image is taken. In this paper, a new scene recognition method using the…