1 code implementation • 24 Oct 2023 • Chaewon Park, Soohwan Kim, Kyubyong Park, Kunwoo Park
This resource is the largest offensive language corpus in Korean and is the first to offer target-specific ratings on a three-point Likert scale, enabling the detection of hate expressions in Korean across varying degrees of offensiveness.
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 • 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 • CVPR 2023 • MyeongAh Cho, Minjung Kim, Sangwon Hwang, Chaewon Park, Kyungjae Lee, Sangyoun Lee
Furthermore, as the relationship between context and motion is important in order to identify the anomalies in complex and diverse scenes, we propose a Context--Motion Interrelation Module (CoMo), which models the relationship between the appearance of the surroundings and motion, rather than utilizing only temporal dependencies or motion information.
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 • 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
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.
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.
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.
no code implementations • 13 Feb 2022 • Chaewon Park, Minhyeok Lee, MyeongAh Cho, Sangyoun Lee
Moreover, MOLoss urges the model to focus on learning normal objects captured within RandomSEMO by amplifying the loss on the pixels near the moving objects.
no code implementations • 13 Oct 2021 • Chaewon Park, Minhyeok Lee, MyeongAh Cho, Sangyoun Lee
1) Indiscriminately integrating the encoder feature, which contains spatial information for multiple objects, and the decoder feature, which contains global information of the salient object, is likely to convey unnecessary details of non-salient objects to the decoder, hindering saliency detection.
Ranked #1 on RGB Salient Object Detection on PASCAL-S
no code implementations • 16 Jun 2021 • Minhyeok Lee, Sangwon Hwang, Chaewon Park, Sangyoun Lee
Monocular depth estimation is an especially important task in robotics and autonomous driving, where 3D structural information is essential.
1 code implementation • 16 Jun 2021 • Chaewon Park, MyeongAh Cho, Minhyeok Lee, Sangyoun Lee
Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos.
Anomaly Detection In Surveillance Videos Optical Flow Estimation +1
1 code implementation • ICCV 2021 • Kwang Hee Lee, Chaewon Park, Junghyun Oh, Nojun Kwak
LFI-CAM generates an attention map for visual explanation during forward propagation, at the same time, leverages the attention map to improve the classification performance through the attention mechanism.