Demo Data from EDBT 2026 / reviewers in the wild / expert
Duy-Nam Ly
dblp:329/7932
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
0000-0003-4304-2334ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 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.
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% |
Topics — the 2 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Multimedia analysis and retrieval › multimedia analysis › multimedia content description
image captioning |
0.9 | 1 | 2025 | Event-Enriched Image Analysis Grand Challenge At ACM Multimedia 2025 · ACM Multimedia 2025 |
Multimedia analysis and retrieval › multimedia analysis
multimodal understanding |
0.3 | 1 | 2025 | Event-Enriched Image Analysis Grand Challenge At ACM Multimedia 2025 · ACM Multimedia 2025 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Event-Enriched Image Analysis Grand Challenge At ACM Multimedia 2025abstractThe Event-Enriched Image Analysis (EVENTA) Grand Challenge, hosted at ACM Multimedia 2025, introduces the first large-scale benchmark for event-level multimodal understanding. Traditional captioning and retrieval tasks largely focus on surface-level recognition of people, objects, and scenes, often overlooking the contextual and semantic dimensions that define real-world events. EVENTA addresses this gap by integrating contextual, temporal, and semantic information to capture the who, when, where, what, and why behind an image. Built upon the OpenEvents V1 dataset, the challenge features two tracks: Event-Enriched Image Retrieval and Captioning, and Event-Based Image Retrieval. A total of 45 teams from six countries participated, with evaluation conducted through Public and Private Test phases to ensure fairness and reproducibility. The top three teams were invited to present their solutions at ACM Multimedia 2025. EVENTA establishes a foundation for context-aware, narrative-driven multimedia AI, with applications in journalism, media analysis, cultural archiving, and accessibility. Further details about the challenge are available at the official homepage: https://ltnghia.github.io/eventa/eventa-2025. Thien-Phuc Tran, Minh-Quang Nguyen, Minh-Triet Tran, Tam V. Nguyen 0002, Trong-Le Do, Duy-Nam Ly, Viet-Tham Huynh, Khanh-Duy Le, Mai-Khiem Tran, Trung-Nghia Le |
ACM Multimedia | 6 |