1 code implementation • 12 Mar 2024 • Jungho Lee, Dogyoon Lee, Minhyeok Lee, Donghyung Kim, Sangyoun Lee
Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity.
no code implementations • 29 Nov 2023 • Minhyeok Lee, Dogyoon Lee, Jungho Lee, Suhwan Cho, Heeseung Choi, Ig-Jae Kim, Sangyoun Lee
While these methods match language features with image features to effectively identify likely target objects, they often struggle to correctly understand contextual information in complex and ambiguous sentences and scenes.
1 code implementation • 15 Mar 2023 • Minhyeok Lee, Suhwan Cho, Dogyoon Lee, Chaewon Park, Jungho Lee, Sangyoun Lee
Unsupervised video object segmentation aims to segment the most prominent object in a video sequence.
no code implementations • 8 Mar 2023 • Seunghoon Lee, Suhwan Cho, Dogyoon Lee, Minhyeok Lee, Sangyoun Lee
In recent works, two approaches for UVOS have been discussed that can be divided into: appearance and appearance-motion-based methods, which have limitations respectively.
1 code implementation • 22 Nov 2022 • Suhwan Cho, Minhyeok Lee, Seunghoon Lee, Dogyoon Lee, Heeseung Choi, Ig-Jae Kim, Sangyoun Lee
Unsupervised video object segmentation (VOS) aims to detect and segment the most salient object in videos.
Ranked #1 on Unsupervised Video Object Segmentation on FBMS test
no code implementations • 22 Nov 2022 • Minhyeok Lee, Suhwan Cho, Chaewon Park, Dogyoon Lee, Jungho Lee, Sangyoun Lee
The proposed DPS-Net utilizes a Deformable Point Sampling transformer (DPS transformer) that can effectively capture sparse local boundary information of significant object boundaries in COD using a deformable point sampling method.
1 code implementation • CVPR 2023 • Dogyoon Lee, Minhyeok Lee, Chajin Shin, Sangyoun Lee
The few studies that have investigated NeRF for blurred images have not considered geometric and appearance consistency in 3D space, which is one of the most important factors in 3D reconstruction.
1 code implementation • ICCV 2023 • Jungho Lee, Minhyeok Lee, Dogyoon Lee, Sangyoun Lee
Graph convolutional networks (GCNs) are the most commonly used methods for skeleton-based action recognition and have achieved remarkable performance.
Ranked #4 on Skeleton Based Action Recognition on NTU RGB+D 120
1 code implementation • 5 Aug 2022 • Chajin Shin, Hyeongmin Lee, Hanbin Son, Sangjin Lee, Dogyoon Lee, Sangyoun Lee
Then, we increase the receptive field to make the adaptive rescaling module consider the spatial correlation.
no code implementations • 27 Jul 2021 • Sungmin Woo, Dogyoon Lee, Sangwon Hwang, Woojin Kim, Sangyoun Lee
In this paper, we present Multidimensional Kernel Convolution (MKConv), a novel convolution operator that learns to transform the point feature representation from a vector to a multidimensional matrix.
Ranked #13 on 3D Part Segmentation on ShapeNet-Part
1 code implementation • 14 Feb 2021 • Minhyeok Lee, Junhyeop Lee, Dogyoon Lee, Woojin Kim, Sangwon Hwang, Sangyoun Lee
Modern deep learning methods achieve high performance in lane detection, but it is still difficult to accurately detect lanes in challenging situations such as congested roads and extreme lighting conditions.
Ranked #42 on Lane Detection on CULane
1 code implementation • CVPR 2021 • Dogyoon Lee, Jaeha Lee, Junhyeop Lee, Hyeongmin Lee, Minhyeok Lee, Sungmin Woo, Sangyoun Lee
Data augmentation is an effective regularization strategy to alleviate the overfitting, which is an inherent drawback of the deep neural networks.
Ranked #3 on 3D Point Cloud Classification on ModelNet40-C
no code implementations • 27 May 2020 • Sungmin Woo, Sangwon Hwang, Woojin Kim, Junhyeop Lee, Dogyoon Lee, Sangyoun Lee
Recently, researchers have been leveraging LiDAR point cloud for higher accuracy in 3D vehicle detection.