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.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15Existing 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
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
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
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,
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
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
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
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
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
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
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
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
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
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