Search Results for author: MyeongAh Cho

Found 15 papers, 4 papers with code

Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation

1 code implementation26 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.

Object Optical Flow Estimation +3

Look Around for Anomalies: Weakly-Supervised Anomaly Detection via Context-Motion Relational Learning

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.

Relational Reasoning Supervised Anomaly Detection +2

Feature Disentanglement Learning with Switching and Aggregation for Video-based Person Re-Identification

no code implementations16 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.

Disentanglement Video-Based Person Re-Identification

Occluded Person Re-Identification via Relational Adaptive Feature Correction Learning

no code implementations9 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.

Person Re-Identification

Pixel-Level Equalized Matching for Video Object Segmentation

no code implementations4 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.

Object Semantic Segmentation +2

NIR-to-VIS Face Recognition via Embedding Relations and Coordinates of the Pairwise Features

no code implementations4 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.

Face Recognition Relation

N-RPN: Hard Example Learning for Region Proposal Networks

no code implementations3 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.

Region Proposal

RandomSEMO: Normality Learning Of Moving Objects For Video Anomaly Detection

no code implementations13 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.

Anomaly Detection Superpixels +1

Saliency Detection via Global Context Enhanced Feature Fusion and Edge Weighted Loss

no code implementations13 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.

Object object-detection +3

A NIR-to-VIS face recognition via part adaptive and relation attention module

no code implementations1 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.

Face Recognition Heterogeneous Face Recognition +1

Unsupervised Video Anomaly Detection via Normalizing Flows with Implicit Latent Features

no code implementations15 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.

Anomaly Detection Density Estimation +3

Learning Temporally Invariant and Localizable Features via Data Augmentation for Video Recognition

1 code implementation13 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.

Action Recognition Data Augmentation +1

Relational Deep Feature Learning for Heterogeneous Face Recognition

no code implementations2 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.

Face Recognition Heterogeneous Face Recognition

CRVOS: Clue Refining Network for Video Object Segmentation

1 code implementation10 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.

Object Segmentation +4

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