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 • 26 Sep 2023 • Suhwan Cho, Minhyeok Lee, Jungho Lee, MyeongAh Cho, Sangyoun Lee
Unsupervised video object segmentation (VOS) is a task that aims to detect the most salient object in a video without external guidance about the object.
no code implementations • 17 Mar 2023 • Yongwoo Lee, Minhyeok Lee, Suhwan Cho, Sangyoun Lee
Salient object detection (SOD) is a task that involves identifying and segmenting the most visually prominent object in an image.
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.
no code implementations • 28 Feb 2023 • Sangjin Lee, Suhwan Cho, Sangyoun Lee
Usually, a video sequence and object segmentation masks for all frames are required as the input for this task.
no code implementations • 20 Feb 2023 • Chaewon Park, Minhyeok Lee, Suhwan Cho, Donghyeong Kim, Sangyoun Lee
Image reconstruction-based anomaly detection has recently been in the spotlight because of the difficulty of constructing anomaly datasets.
no code implementations • 9 Dec 2022 • Minjung Kim, MyeongAh Cho, Heansung Lee, Suhwan Cho, Sangyoun Lee
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 crowded scenes.
1 code implementation • ICCV 2023 • Jungho Lee, Minhyeok Lee, Suhwan Cho, Sungmin Woo, Sungjun Jang, Sangyoun Lee
In this paper, we propose the Spatio-Temporal Curve Network (STC-Net) to effectively leverage the spatio-temporal dependency of the human skeleton.
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 • 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
1 code implementation • 14 Nov 2022 • Donghyeong Kim, Chaewon Park, Suhwan Cho, Sangyoun Lee
Feature embedding-based methods have shown exceptional performance in detecting industrial anomalies by comparing features of target images with normal images.
Ranked #29 on Anomaly Detection on MVTec AD
1 code implementation • 8 Sep 2022 • Minhyeok Lee, Suhwan Cho, Seunghoon Lee, Chaewon Park, Sangyoun Lee
The proposed model effectively extracts the RGB and motion information by extracting superpixel-based component prototypes from the input RGB images and optical flow maps.
Ranked #5 on Unsupervised Video Object Segmentation on FBMS test
2 code implementations • 4 Sep 2022 • Suhwan Cho, Minhyeok Lee, Seunghoon Lee, Chaewon Park, Donghyeong Kim, Sangyoun Lee
Unsupervised video object segmentation (VOS) aims to detect the most salient object in a video sequence at the pixel level.
no code implementations • 4 Sep 2022 • Suhwan Cho, Woo Jin Kim, MyeongAh Cho, Seunghoon Lee, Minhyeok Lee, Chaewon Park, Sangyoun Lee
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.
1 code implementation • 16 Jul 2022 • Minhyeok Lee, Chaewon Park, Suhwan Cho, Sangyoun Lee
However, despite advances in deep learning-based methods, RGB-D SOD is still challenging due to the large domain gap between an RGB image and the depth map and low-quality depth maps.
Ranked #3 on RGB-D Salient Object Detection on NJU2K
1 code implementation • 14 Jul 2022 • Suhwan Cho, Heansung Lee, Minhyeok Lee, Chaewon Park, Sungjun Jang, Minjung Kim, Sangyoun Lee
Semi-supervised video object segmentation (VOS) aims to densely track certain designated objects in videos.
1 code implementation • 4 Oct 2021 • Suhwan Cho, Heansung Lee, Minjung Kim, Sungjun Jang, Sangyoun Lee
Before finding the best matches for the query frame pixels, the optimal matches for the reference frame pixels are first considered to prevent each reference frame pixel from being overly referenced.
no code implementations • 26 Oct 2020 • Tae-young Chung, Heansung Lee, Myeong Ah Cho, Suhwan Cho, Sangyoun Lee
So in this paper, we propose a novel self-supervised learning method using a lot of short videos which has no human labeling, and improve the tracking performance through the re-identification network trained in the self-supervised manner to solve the lack of training data problem.
no code implementations • 15 Oct 2020 • MyeongAh Cho, Taeoh Kim, Woo Jin Kim, Suhwan Cho, Sangyoun Lee
For the complex distribution of normal scenes, we suggest normal density estimation of ITAE features through normalizing flow (NF)-based generative models to learn the tractable likelihoods and identify anomalies using out of distribution detection.
no code implementations • 18 Sep 2020 • Suhwan Cho, Heansung Lee, Sungmin Woo, Sungjun Jang, Sangyoun Lee
Semi-supervised video object segmentation (VOS) aims to segment arbitrary target objects in video when the ground truth segmentation mask of the initial frame is provided.
1 code implementation • 10 Feb 2020 • Suhwan Cho, MyeongAh Cho, Tae-young Chung, Heansung Lee, Sangyoun Lee
The encoder-decoder based methods for semi-supervised video object segmentation (Semi-VOS) have received extensive attention due to their superior performances.
Ranked #60 on Semi-Supervised Video Object Segmentation on DAVIS 2016