Go to ACL ARR 2026 January homepageLongInsightBench: A Comprehensive Benchmark for Evaluating Omni-Modal Models on Human-Centric Long-Video Understanding.
Keywords: human-centric video understanding, multi-modal fusion, omni-modal language model
Abstract: We introduce \textbf{LongInsightBench}, the first benchmark designed to assess models' ability to understand long videos, with a focus on human language, viewpoints, actions, and other contextual elements, while integrating \textbf{visual, audio, and text} modalities. Our benchmark excels in three key areas: \textbf{a) Long-Duration, Human-Centric Videos:} We carefully selected approximately 1,000 videos from open-source datasets FineVideo based on duration limit and multi-modal information density, focusing on content like lectures, interviews, and vlogs, which contain rich human-centric semantic and contextual attributes. \textbf{b) Diverse and Challenging Task Scenarios:} We have designed six challenging task scenarios, including both Intra-Event and Inter-Event Tasks. \textbf{c) Rigorous and Comprehensive Quality Assurance Pipelines:} We have developed a three-step, semi-automated data quality assurance pipeline to ensure the difficulty and validity of the synthesized questions and answer options. Based on LongInsightBench, we designed a series of experiments. which shows that Omni-modal models(OLMs) still face challenge in tasks requiring precise temporal localization (T-Loc) and long-range causal inference (CE-Caus). Surprisingly, extended experiments reveal the information loss in modal fusion of OLMs, which we called the \textbf{\textit{Fusion Deficit Paradox}}.
Paper Type: Long
Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond
Research Area Keywords: cross-modal content generation,vision question answering,speech and vision,video processing,multimodality
Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis
Languages Studied: English
Submission Number: 9057
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