Event-Enriched Image Analysis Grand Challenge At ACM Multimedia 2025

The 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.

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Publication Details

Published
2025-10-25
DOI
https://doi.org/10.1145/3746027.3762067
Primary Topic
Multimodal Machine Learning Applications
Type
article
Field-Weighted Citation Impact
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article

Event-Enriched Image Analysis Grand Challenge At ACM Multimedia 2025

Viet-Tham Huynh, Mai-Khiem Tran, Tam Nguyen, Minh–Triet Tran et al.
Multimodal Machine Learning Applications
article

Event-Enriched Image Analysis Grand Challenge At ACM Multimedia 2025

Viet-Tham Huynh, Mai-Khiem Tran, Tam Nguyen, Minh–Triet Tran, Trong-Le Do, Trung-Nghia Le, Khanh-Duy Le, Duy-Nam Ly, Thien-Phuc Tran, M. Nguyen
article en

Abstract

The 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.

Vietnam National University Ho Chi Minh City (VN), University of Dayton (US), Ho Chi Minh City University of Science (VN)
Openalex Percentile: Top 70%
Multimodal Machine Learning Applications
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Event-Enriched Image Analysis Grand Challenge At ACM Multimedia 2025 — Viet-Tham Huynh, Mai-Khiem Tran, et al. · (2025) | TGRS Research Map | TGRS