Event-Enriched Image Analysis Grand Challenge at ACM Multimedia 2025
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911121757175808 |
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| author | Tran, Thien-Phuc Nguyen, Minh-Quang Tran, Minh-Triet Nguyen, Tam V. Do, Trong-Le Ly, Duy-Nam Huynh, Viet-Tham Le, Khanh-Duy Tran, Mai-Khiem Le, Trung-Nghia |
| author_facet | Tran, Thien-Phuc Nguyen, Minh-Quang Tran, Minh-Triet Nguyen, Tam V. Do, Trong-Le Ly, Duy-Nam Huynh, Viet-Tham Le, Khanh-Duy Tran, Mai-Khiem Le, Trung-Nghia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_18904 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Event-Enriched Image Analysis Grand Challenge at ACM Multimedia 2025 Tran, Thien-Phuc Nguyen, Minh-Quang Tran, Minh-Triet Nguyen, Tam V. Do, Trong-Le Ly, Duy-Nam Huynh, Viet-Tham Le, Khanh-Duy Tran, Mai-Khiem Le, Trung-Nghia Computer Vision and Pattern Recognition 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. |
| title | Event-Enriched Image Analysis Grand Challenge at ACM Multimedia 2025 |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.18904 |