Demo Data from VLDB 2026 / reviewers in the wild / expert
Sunung Mun
dblp:217/9153
· DBLP profile ↗
3ranked-venue papers
1as first author
2since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 2 since 2021Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Human-computer interaction and ubiquitous computing · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
2 papers |
Generative modeling · 50% Trustworthy machine learning · 22% Transfer learning and domain adaptation · 22% | |
| Human-computer interaction and pervasive computing
1 paper |
Haptics and multimodal interaction · 50% Accessibility and assistive technology · 50% |
Topics — the 7 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling › diffusion model › image editing
diffusion-based image editing |
0.9 | 1 | 2025 | Addressing Text Embedding Leakage in Diffusion-Based Image Editing · ICCV 2025 |
Machine learning › Generative modeling
diffusion model |
0.9 | 1 | 2025 | Addressing Text Embedding Leakage in Diffusion-Based Image Editing · ICCV 2025 |
Machine learning › Trustworthy machine learning
robustness |
0.8 | 1 | 2024 | MedBN: Robust Test-Time Adaptation against Malicious Test Samples · CVPR 2024 |
Machine learning › Transfer learning and domain adaptation
test-time adaptation |
0.8 | 1 | 2024 | MedBN: Robust Test-Time Adaptation against Malicious Test Samples · CVPR 2024 |
Accessibility and assistive technology › assistive technology
sensory substitution |
0.3 | 1 | 2018 | Substituting Motion Effects with Vibrotactile Effects for 4D Experiences · CHI 2018 |
Haptics and multimodal interaction › haptic feedback
vibrotactile feedback |
0.3 | 1 | 2018 | Substituting Motion Effects with Vibrotactile Effects for 4D Experiences · CHI 2018 |
Machine learning › Representation and self-supervised learning
text embedding |
0.3 | 1 | 2025 | Addressing Text Embedding Leakage in Diffusion-Based Image Editing · ICCV 2025 |
Methods — techniques the papers use, named apart from their topics
diffusion model · 0.9median batch normalization · 0.8batch normalization statistics · 0.8user study · 0.7sensory substitution algorithm · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Addressing Text Embedding Leakage in Diffusion-Based Image Editing
Sunung Mun, Jinhwan Nam, Sunghyun Cho, Jungseul Ok |
ICCV | 1 |
| 2024 | MedBN: Robust Test-Time Adaptation against Malicious Test SamplesabstractTest-time adaptation (TTA) has emerged as a promising solution to address performance decay due to unforeseen distribution shifts between training and test data. While recent TTA methods excel in adapting to test data variations, such adaptability exposes a model to vulnerability against ma-licious examples. Indeed, previous studies have uncovered security vulnerabilities within TTA even when a small proportion of the test batch is maliciously manipulated. In response to the emerging threat, we propose median batch normal-ization (MedBN), leveraging the robustness of the median for statistics estimation within the batch normalization layer during test-time inference. Our method is algorithm-agnostic, thus allowing seamless integration with existing TTA frame-works. Our experimental results on benchmark datasets, in-cluding CIFAR10-C, CIFAR100-C, and ImageNet-C, con-sistently demonstrate that MedBN outperforms existing approaches in maintaining robust performance across different attack scenarios, encompassing both instant and cumulative attacks. Through extensive experiments, we show that our approach sustains the performance even in the absence of at-tacks, achieving a practical balance between robustness and performance. Our code is available at https://github.com/ml-postech/MedBN-robust-test-time-adaptation. Hyejin Park 0002, Jeongyeon Hwang, Sunung Mun, Sangdon Park 0001, Jungseul Ok |
CVPR | 3 |
| 2018 | Substituting Motion Effects with Vibrotactile Effects for 4D ExperiencesabstractIn this paper, we present two methods to substitute motion effects using vibrotactile effects in order to improve the 4D experiences of viewers. This work was motivated by the needs of more affordable 4D systems for individual users. Our sensory substitution algorithms convert motion commands to vibrotactile commands to a grid display that uses multiple actuators. While one method is based on the fundamental principle of vestibular feedback, the other method makes use of intuitive visually-based mapping from motion to vibrotactile stimulation. We carried out a user study and could confirm the effectiveness of our substitution methods in improving 4D experiences. To our knowledge, this is the first study that investigated the feasibility of replacing motion effects using much simpler and less expensive vibrotactile effects. Jongman Seo, Sunung Mun, Jaebong Lee, Seungmoon Choi |
CHI | 2 |