ReXTime: A Benchmark Suite for Reasoning-Across-Time in Videos

Jr-Jen Chen, Yu-Chien Liao, Hsi-Che Lin, Yu-Chu Yu, Yen-Chun Chen, Yu-Chiang Frank Wang

Keywords: Video Question Answering, Video Temporal Reasoning, Multi-modal Large Language Model, Video Moment Localization

TL;DR: ReXTime benchmarks AI models' temporal reasoning across video events, revealing a significant accuracy gap between frontier MLLMs and humans.

Abstract:

We introduce ReXTime, a benchmark designed to rigorously test AI models' ability to perform temporal reasoning within video events.
Specifically, ReXTime focuses on reasoning across time, i.e. human-like understanding when the question and its corresponding answer occur in different video segments. This form of reasoning, requiring advanced understanding of cause-and-effect relationships across video segments, poses significant challenges to even the frontier multimodal large language models. To facilitate this evaluation, we develop an automated pipeline for generating temporal reasoning question-answer pairs, significantly reducing the need for labor-intensive manual annotations. Our benchmark includes 921 carefully vetted validation samples and 2,143 test samples, each manually curated for accuracy and relevance. Evaluation results show that while frontier large language models outperform academic models, they still lag behind human performance by a significant 14.3\% accuracy gap. Additionally, our pipeline creates a training dataset of 9,695 machine generated samples without manual effort, which empirical studies suggest can enhance the across-time reasoning via fine-tuning.

Submission Number: 1551