# OpenEvents V1: Large-Scale Benchmark Dataset for Multimodal Event Grounding

arXiv: https://arxiv.org/abs/2506.18372 · HTML: https://mlsanity.com/paper/2506.18372/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 23 Jun 2025.

## Builds on (most cited first, 14 of 14)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Gemma 3 Technical Report](https://mlsanity.com/paper/2503.19786/) (2025)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [Reproducible scaling laws for contrastive language-image learning](https://mlsanity.com/paper/2212.07143/) (2022)
- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [Stacked Cross Attention for Image-Text Matching](https://mlsanity.com/paper/1803.08024/) (2018)
- [WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning](https://mlsanity.com/paper/2103.01913/) (2021)
- [SmolVLM: Redefining small and efficient multimodal models](https://mlsanity.com/paper/2504.05299/) (2025)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval](https://mlsanity.com/paper/1805.08661/) (2018)
- [RAMM: Retrieval-augmented Biomedical Visual Question Answering with Multi-modal Pre-training](https://mlsanity.com/paper/2303.00534/) (2023)

## Built on by (most cited first, 8 of 8)

- [Event-Enriched Image Analysis Grand Challenge at ACM Multimedia 2025](https://mlsanity.com/paper/2508.18904/) (2025)
- [ZSE-Cap: A Zero-Shot Ensemble for Image Retrieval and Prompt-Guided Captioning](https://mlsanity.com/paper/2507.20564/) (2025)
- [EVENT-Retriever: Event-Aware Multimodal Image Retrieval for Realistic Captions](https://mlsanity.com/paper/2509.00751/) (2025)
- [ReCap: Event-Aware Image Captioning with Article Retrieval and Semantic Gaussian Normalization](https://mlsanity.com/paper/2509.01259/) (2025)
- [Beyond Vision: Contextually Enriched Image Captioning with Multi-Modal Retrieval](https://mlsanity.com/paper/2512.20042/) (2025)
- [Leveraging Lightweight Entity Extraction for Scalable Event-Based Image Retrieval](https://mlsanity.com/paper/2512.21221/) (2025)
- [CIAN: Multi-Stage Framework for Event-Enriched Image Captioning via Retrieval-Augmented Generation](https://mlsanity.com/paper/2606.17430/) (2026)
- [Hierarchical Multi-Modal Retrieval for Knowledge-Grounded News Image Captioning](https://mlsanity.com/paper/2606.18553/) (2026)

## Abstract (arXiv)

We introduce OpenEvents V1a large-scale benchmark dataset designed to advance event-centric vision-language understanding. Unlike conventional image captioning and retrieval datasets that focus on surface-level descriptions, OpenEvents V1 dataset emphasizes contextual and temporal grounding through three primary tasks: (1) generating rich, event-aware image captions, (2) retrieving event-relevant news articles from image queries, and (3) retrieving event-relevant images from narrative-style textual queries. The dataset comprises over 200,000 news articles and 400,000 associated images sourced from CNN and The Guardian, spanning diverse domains and time periods. We provide extensive baseline results and standardized evaluation protocols for all tasks. OpenEvents V1 establishes a robust foundation for developing multimodal AI systems capable of deep reasoning over complex real-world events. The dataset is publicly available at https://ltnghia.github.io/eventa/openevents-v1.