EDBT 2026 Demo / reviewers in the wild / expert
JooYoung Seo
dblp:413/1314
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0002-4064-6012ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
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.
| Human-computer interaction and pervasive computing
1 paper |
Accessibility and assistive technology · 50% Human-AI interaction · 50% | |
| Artificial intelligence
1 paper |
Vision and language · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Accessibility and assistive technology › assistive technology for visual impairment
assistive technology for blind and low-vision users |
0.9 | 1 | 2025 | AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multimodal Information Between Reality and Videos · UIST 2025 |
Human-AI interaction
mixed-initiative interaction |
0.9 | 1 | 2025 | AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multimodal Information Between Reality and Videos · UIST 2025 |
Computer vision › Vision and language
multimodal grounding |
0.3 | 1 | 2025 | AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multimodal Information Between Reality and Videos · UIST 2025 |
Methods — techniques the papers use, named apart from their topics
wearable camera · 1.7video grounding · 1.7multimodal large language model · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | AROMA: Mixed-Initiative AI Assistance for Non-Visual Cooking by Grounding Multimodal Information Between Reality and VideosabstractVideos offer rich audiovisual information that can support people in performing activities of daily living (ADLs), but they remain largely inaccessible to blind or low-vision (BLV) individuals.In cooking, BLV people often rely on non-visual cues-such as touch, taste, and smell-to navigate their environment, making it difficult to follow UIST '25, September 28-October 01, 2025, Busan, Republic of Korea Ning et al.the predominantly audiovisual instructions found in video recipes.To address this problem, we introduce Aroma, an AI system that provides timely responses to the user based on real-time, contextaware assistance by integrating non-visual cues perceived by the user, a wearable camera feed, and video recipe content.Aroma uses a mixed-initiative approach: it responds to user requests while also proactively monitoring the video stream to offer timely alerts and guidance.This collaborative design leverages the complementary strengths of the user and AI system to align the physical environment with the video recipe, helping the user interpret their current state and make sense of the steps.We evaluated Aroma through a study with eight BLV participants and offered insights for designing interactive AI systems to support BLV individuals in performing ADLs. Zheng Ning, Leyang Li, Daniel Killough, JooYoung Seo, Patrick Carrington, Yapeng Tian, Yuhang Zhao 0001, Franklin Mingzhe Li, Toby Jia-Jun Li |
UIST | 4 |