This paper presents question-answering on dense video events, a novel task that answers and grounds dense-event questions in long videos, thus challenging MLLMs to faithfully comprehend and reason about multiple events over extended periods of time. To facilitate the study, we construct DeVE-QA -- a dataset featuring 78K questions about 26K events on 10.6K long videos. Our benchmarking shows that state-of-the-art MLLMs struggle on DeVE-QA. For improvement, we propose DeVi, a novel training-free MLLM approach that highlights a hierarchical captioning module, a temporal event memory module, and a self-consistency checking module to respectively detect, contextualize and memorize, and ground dense-events in long videos for question answering. Extensive experiments show that DeVi is superior at answering dense-event questions and grounding relevant video moments. Compared with existing MLLMs, it achieves a remarkable increase of 4.8% and 2.1% for G(round)QA accuracy on DeVE-QA~and NExT-GQA, respectively. Our data and code will be released upon acceptance.
View on arXiv@article{qin2025_2409.04388,
title={ Question-Answering Dense Video Events },
author={ Hangyu Qin and Junbin Xiao and Angela Yao },
journal={arXiv preprint arXiv:2409.04388},
year={ 2025 }
}