How are we able to learn about complex current events just from short snippets of video? While natural language enables straightforward ways to represent under-specified, partially observable events, visual data does not facilitate analogous methods and, consequently, introduces unique challenges in event understanding. With the growing prevalence of vision-capable AI agents, these systems must be able to model events from collections of unstructured video data. To tackle robust event modeling in multimodal settings, we introduce a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task. We propose a corresponding benchmark for this task, MultiVENT-G, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities. We propose a collection of LLM-driven approaches to the task of multimodal event analysis, and evaluate them on MultiVENT-G. Results illustrate the challenges that abstract event understanding poses and demonstrates promise in event-centric video-language systems.
@article{arxiv.2410.05267,
title = {Grounding Partially-Defined Events in Multimodal Data},
author = {Kate Sanders and Reno Kriz and David Etter and Hannah Recknor and Alexander Martin and Cameron Carpenter and Jingyang Lin and Benjamin Van Durme},
journal= {arXiv preprint arXiv:2410.05267},
year = {2024}
}