Current datasets for long-form video understanding often fall short of providing genuine long-form comprehension challenges, as many tasks derived from these datasets can be successfully tackled by analyzing just one or a few random frames from a video. To address this issue, we present a novel dataset and benchmark, CinePile, specifically designed for authentic long-form video understanding. This paper details our innovative approach for creating a question-answer dataset, utilizing advanced LLMs with human-in-the-loop and building upon human-generated raw data. Our comprehensive dataset comprises 305,000 multiple-choice questions (MCQs), covering various visual and multimodal aspects, including temporal comprehension, understanding human-object interactions, and reasoning about events or actions within a scene. Additionally, we fine-tuned open-source Video-LLMs on the training split and evaluated both open-source and proprietary video-centric LLMs on the test split of our dataset. The findings indicate that although current models underperform compared to humans, fine-tuning these models can lead to significant improvements in their performance.
@article{arxiv.2405.08813,
title = {CinePile: A Long Video Question Answering Dataset and Benchmark},
author = {Ruchit Rawal and Khalid Saifullah and Miquel Farré and Ronen Basri and David Jacobs and Gowthami Somepalli and Tom Goldstein},
journal= {arXiv preprint arXiv:2405.08813},
year = {2024}
}
Comments
Project page with all the artifacts - https://ruchitrawal.github.io/cinepile/. Updated version with adversarial refinement pipeline and more model evaluations