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 • 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 • 16 Dec 2022 • Minjung Kim, MyeongAh Cho, Sangyoun Lee
In video person re-identification (Re-ID), the network must consistently extract features of the target person from successive frames.
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.
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.
no code implementations • 4 Aug 2022 • MyeongAh Cho, Tae-young Chun, g Taeoh Kim, Sangyoun Lee
With the proposed module, we achieve 14. 81% rank-1 accuracy and 15. 47% verification rate of 0. 1% FAR improvements compare to two baseline models.
no code implementations • 3 Aug 2022 • MyeongAh Cho, Tae-young Chung, Hyeongmin Lee, Sangyoun Lee
The region proposal task is to generate a set of candidate regions that contain an object.
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
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
no code implementations • 1 Feb 2021 • Rushuang Xu, MyeongAh Cho, Sangyoun Lee
In the face recognition application scenario, we need to process facial images captured in various conditions, such as at night by near-infrared (NIR) surveillance cameras.
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.
1 code implementation • 13 Aug 2020 • Taeoh Kim, Hyeongmin Lee, MyeongAh Cho, Ho Seong Lee, Dong Heon Cho, Sangyoun Lee
Based on our novel temporal data augmentation algorithms, video recognition performances are improved using only a limited amount of training data compared to the spatial-only data augmentation algorithms, including the 1st Visual Inductive Priors (VIPriors) for data-efficient action recognition challenge.
no code implementations • 2 Mar 2020 • MyeongAh Cho, Taeoh Kim, Ig-Jae Kim, Kyungjae Lee, Sangyoun Lee
Due to the lack of databases, HFR methods usually exploit the pre-trained features on a large-scale visual database that contain general facial information.
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