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Kim Sung-yun

Hanyang University · Computer Science

About the Lab

Professor Kim Sung-yun's research lab specializes in deep metric learning and representation learning, focusing on improving the semantic understanding of embeddings through advanced loss functions and structured supervision. The lab explores methods that bridge the gap between pair-based and proxy-based metric learning, enabling faster convergence and robustness to noisy data. A key research direction involves leveraging continuous and structured labels—such as image captions, scene graphs, and pose descriptions—to learn more nuanced similarity metrics. The lab also investigates knowledge transfer in embedding spaces, particularly through novel loss functions that preserve relational knowledge across domains.

metric learningembedding transfercontrastive losscontinuous supervisionrepresentation learning

Research Overview

Papers
122
Total Citations
1,319
Papers (5y)
41
Primary Field
Computer Science

Research Output Trend

Figures are computed from collected data and may differ slightly.

Publications per year (5y)
41total
2021
2022
2023
2024
2025
Citations per year (5y)
178total
20212022202320242025

Selected Papers

15
1
Article|392 citations·2020
Proxy Anchor Loss for Deep Metric Learning
Sung‐Yeon Kim, Dong-Won Kim, Minsu Cho, Suha Kwak

Existing metric learning losses can be categorized into two classes: pair-based and proxy-based losses. The former class can leverage fine-grained semantic relations between data points, but slows convergence in general due to its high training complexity. In contrast, the latter class enables fast and reliable convergence, but cannot consider the rich data-to-data relations. This paper presents a new proxy-based loss that takes advantages of both pair- and proxy-based methods and overcomes thei

Computer Vision and Pattern RecognitionComputer Science
2
Article|124 citations·2009
Questioning the Stability of Foreign Language Classroom Anxiety and Motivation Across Different Classroom Contexts
Sung‐Yeon Kim
SJR Q1Foreign Language Annals

Abstract: This study examined whether foreign language learner anxiety and motivational goal orientations remained stable across two different classroom contexts: a reading course and a conversation course. The researcher measured anxiety and four types of motivational goal orientations by surveying 59 Korean college students learning English in both courses. A repeated‐measures MANCOVA was used to analyze the responses. The findings indicated that levels of anxiety can vary according to instruc

Language and LinguisticsArts and Humanities
3
Article|93 citations·2019
Deep Metric Learning Beyond Binary Supervision
Sung‐Yeon Kim, Minkyo Seo, Ivan Laptev, Minsu Cho, Suha Kwak

Metric Learning for visual similarity has mostly adopted binary supervision indicating whether a pair of images are of the same class or not. Such a binary indicator covers only a limited subset of image relations, and is not sufficient to represent semantic similarity between images described by continuous and/or structured labels such as object poses, image captions, and scene graphs. Motivated by this, we present a novel method for deep metric learning using continuous labels. First, we propo

Computer Vision and Pattern RecognitionComputer Science
5
Article|48 citations·2004
When the Learner Becomes a Teacher: Foreign Language Anxiety as an Occupational Hazard
Sung‐Yeon Kim, Joohae Kim
SJR Q1English Teaching Practice & Critique

This study investigated classroom teacher anxiety with respect to teaching foreign languages in Korea, and sought to identify sources and symptoms of this anxiety. The subjects' anxiety was measured using the Foreign Language Teaching Anxiety Scale (FLTAS), a scale that was developed for this study. A number of participants acknowledged that they had experienced teaching anxiety in foreign-language classrooms. These results provide evidence for the construct of foreign language teaching anxiety,

Sociology and Political ScienceSocial Sciences
6
Article|41 citations·2004
초등영어교사 불안에 대한 연구
김성연, 김주혜

Teacher anxiety is an important variable to consider with regard to English language teaching, in that it may negatively influence the way teachers make instructional decisions. This study aims to identify sources of primary school English teacher anxiety. The Foreign Language Teaching Anxiety Scale (FLTAS) was used to measure anxiety in Korean primary school English teachers. The study found that the items on which the participants displayed high levels of anxiety were directly related to prima

7
Article|35 citations·2021
Embedding Transfer with Label Relaxation for Improved Metric Learning
Sung‐Yeon Kim, Dong-Won Kim, Minsu Cho, Suha Kwak

This paper presents a novel method for embedding transfer, a task of transferring knowledge of a learned embedding model to another. Our method exploits pairwise similarities between samples in the source embedding space as the knowledge, and transfers them through a loss used for learning target embedding models. To this end, we design a new loss called relaxed contrastive loss, which employs the pairwise similarities as relaxed labels for intersample relations. Our loss provides a rich supervi

Artificial IntelligenceComputer Science
8
Article|32 citations·2008
Five Years of Teaching English Through English: Responses from Teachers and Prospects for Learners
Sung‐Yeon Kim
SJR Q3English TeachingOA

It has been more than 5 years since the TETE policy came into effect. Now is the time to assess the effectiveness of the policy, particularly from the teachers’ point of view. This study aims to identify Korean teachers’ responses to the TETE policy through written survey questionnaires. Their perspectives were analyzed with reference to the type of school they work for, the amount of teaching experience they have, and the frequency with which they use English. The findings indicate that most of

Language and LinguisticsArts and Humanities
10
Article|21 citations·2008
한국 학생들의 영어 어휘 사용 능력 향상을 위한 어휘 지도 방안과 학습 책략
김성연, 이상희

This study aims to investigate the effects of two different methods of collocation-based vocabulary teaching on students’ vocabulary acquisition: a definition-based approach and a task-based approach. Fifty-seven middle school students participated in the study. One group of students was taught with a definition-based approach, while the other group was taught with a productive- task based approach. English vocabulary tests were designed to measure the difference in the students’ vocabulary acqu

11
Article|20 citations·2024
Preservice teachers' learning by design through space construction in the metaverse
Sangmin‐Michelle Lee, Sung‐Yeon Kim
SJR Q1British Journal of Educational TechnologyOA

Abstract Teachers who know what, how and why to teach are essential for successful student learning. However, many preservice teachers (PSTs) lack teaching experience and the ability to integrate theory and practice. To help bridge this gap, this study employed a learning‐by‐design project approach in which 22 Korean PSTs developed lesson plans for middle school English classes, constructed virtual classrooms in the metaverse based on their English lesson plans, and conducted microteaching in th

EducationSocial Sciences
12
Article|18 citations·2022
Self-Taught Metric Learning without Labels
Sung‐Yeon Kim, Dong-Won Kim, Minsu Cho, Suha Kwak
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

We present a novel self-taught framework for unsuper-vised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of an embedding model and learning the model with the predicted relations as pseudo labels. At the heart of our framework lies an algorithm that investigates contexts of data on the embedding space to predict their class-equivalence relations as pseudo labels. The algorithm enables efficient end-to-end training since it

Artificial IntelligenceComputer Science
13
Article|18 citations·2023
HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization
Sung‐Yeon Kim, Boseung Jeong, Suha Kwak

Supervision for metric learning has long been given in the form of equivalence between human-labeled classes. Although this type of supervision has been a basis of metric learning for decades, we argue that it hinders further advances in the field. In this regard, we propose a new regularization method, dubbed HIER, to discover the latent semantic hierarchy of training data, and to deploy the hierarchy to provide richer and more fine-grained supervision than inter-class separability induced by c

Computer Vision and Pattern RecognitionComputer Science
14
Article|18 citations·2025
The impact of AI recommendation quality on service satisfaction: the moderating roles of standardization and customization
Sung‐Yeon Kim, Jinmin Kim
SJR Q1Journal of Services Marketing

Purpose This study aims to investigate how information quality and system quality influence the effectiveness of artificial intelligence (AI)-based recommendation service platforms. It integrates traditional information technology service quality (SQ) metrics with recommendation SQ measures, focusing on their impact on user satisfaction and behavior. This study further examines the moderating effects of standardization and customization on these relationships. Design/methodology/approach This st

Information Systems and ManagementDecision Sciences
15
Article|18 citations·2013
중학교 현장의 영어 말하기 · 쓰기수행평가 적용 사례 연구
김성연, 이호경

This study examined the types of performance assessment middle school English teachers implemented to measure their students’ speaking and writing skills. For data collection, in-depth interviews were conducted with six English teachers in different school districts. The findings of the study indicate that diverse types of tasks were used to assess speaking and writing although the frequency of the tasks used showed some variation. Particularly notable was that the most commonly used task for sp

Research Areas

Language and LinguisticsArtificial IntelligenceComputer Networks and CommunicationsEducationInformation SystemsComputer Vision and Pattern Recognition

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