OmniVideo-100K: A Dataset for Audio-Visual Reasoning through Structured Scripts and Evidence Chains
Introduces OmniVideo-100K, a large-scale dataset with structured scripts and evidence chains, boosting audio-visual reasoning by up to 20.59%.
Key Findings
Methodology
This paper proposes an automated data generation framework combining entity-anchored scripting and clue-guided QA synthesis. The process begins with leveraging Multimodal Large Language Models (MLLMs) to convert raw videos into structured scripts, including summaries, main entity lists, and segment-wise audio-visual descriptions. The entity list acts as a global prior, ensuring referential consistency across segments and linking speech to visual entities. Subsequently, a clue mining strategy extracts cross-segment, multi-modal clues from scripts, forming complex reasoning chains. These clues guide the generation of QA pairs that exhibit long-term temporal dependencies and deep cross-modal interactions. The entire pipeline produces 100,000 high-quality QA pairs in OmniVideo-100K and a human-verified test set OmniVideo-Test. Fine-tuning models like Qwen3-Omni-30B on this dataset yields performance improvements up to 20.59%, demonstrating superior generalization across benchmarks such as Daily-Omni and JointAVBench.
Key Results
- After fine-tuning, Qwen3-Omni-30B achieves 63.56% accuracy on OmniVideo-Test, a 13.86% increase over baseline models. The performance gain is especially notable in reasoning tasks, with improvements of over 12%.
- Models show significant performance gaps between alignment, understanding, and reasoning tasks, with alignment tasks like scene-source alignment remaining challenging (e.g., 37.93%).
- Qualitative analysis indicates that the fine-tuned models better leverage cross-modal clues, resulting in more accurate long-term reasoning and temporal alignment, surpassing baseline reliance on unimodal cues.
Significance
This work addresses fundamental limitations in existing video QA datasets and models by introducing structured scripts and evidence chains, which enable models to perform deep, long-term, and cross-modal reasoning. The creation of OmniVideo-100K provides a scalable, high-quality resource that bridges the gap between short, isolated clips and complex real-world scenarios. The approach enhances the interpretability and factual grounding of model outputs, fostering advancements in video understanding, content retrieval, and human-AI interaction. Its implications extend to industries like entertainment, security, and education, where comprehensive scene comprehension is critical. Overall, this research pushes the boundary of multimodal AI, paving the way for more intelligent and context-aware systems.
Technical Contribution
The key technical contributions include: 1) a novel entity-anchored scripting mechanism that converts raw videos into structured, entity-consistent narratives; 2) a clue mining strategy that extracts multi-modal, multi-segment reasoning cues, enabling models to generate QA pairs with long-term dependencies; 3) an automated pipeline that produces 100,000 high-quality, evidence-grounded QA pairs, significantly reducing manual annotation efforts; 4) multi-task fine-tuning techniques that enhance model robustness across alignment, understanding, and reasoning tasks. These innovations collectively establish a new paradigm for scalable, deep multimodal reasoning data generation and model training.
Novelty
This study is the first to systematically integrate structured scripts and evidence chains into large-scale multimodal QA data generation. Unlike prior datasets that focus on short clips or isolated events, OmniVideo-100K emphasizes long-term, cross-segment, and cross-modal reasoning. The automatic pipeline, combining entity anchoring and clue-guided QA synthesis, enables scalable creation of complex, high-quality data. This approach introduces a new methodology for fostering deep multimodal understanding, setting a new standard in the field.
Limitations
- Despite improvements, the entity recognition and script generation still face challenges in highly complex scenes involving multiple overlapping entities or ambiguous actions, which can lead to inaccuracies in references and descriptions.
- The clue mining process relies on predefined task categories, limiting its ability to generalize to unforeseen reasoning types or novel scenarios without further adaptation.
- While large-scale, the automatic data generation may contain noise and biases inherited from the models used, potentially affecting downstream training and evaluation. Further refinement and human-in-the-loop validation are needed to improve data quality.
AI Executive Summary
In the rapidly evolving field of multimodal video understanding, existing question-answering systems predominantly rely on short clips and isolated descriptions, which severely limit their capacity to comprehend complex, long-term scenarios. These approaches often segment videos into small fragments, generating separate audio and visual descriptions that disconnect the inherent associations between sounds and their sources. Consequently, models struggle with maintaining entity consistency across segments and capturing causal or temporal relationships spanning longer durations.
This paper introduces OmniVideo-100K, a comprehensive dataset designed to overcome these limitations through a novel framework that combines structured scripting and evidence chain construction. The core idea is to transform raw videos into structured scripts that include summaries, main entity lists, and segment-wise descriptions integrating speech, sounds, and visual cues. This transformation is achieved using advanced multimodal large language models (MLLMs), which analyze the video content holistically, ensuring entity consistency and accurate source attribution.
Building on this structured representation, the authors develop a clue-guided question-answering strategy. Instead of generating QA pairs directly from lengthy descriptions, the model first mines cross-segment, multi-modal clues that form reasoning chains. These clues serve as anchors, guiding the model to produce QA pairs that require understanding long-term dependencies and deep cross-modal interactions. This approach significantly enhances the model’s reasoning depth and temporal alignment capabilities.
The pipeline automates the creation of a large-scale dataset containing 100,000 QA pairs, which are then used to fine-tune state-of-the-art models such as Qwen3-Omni-30B and VITA-1.5. Experimental results demonstrate performance improvements of up to 20.59% on the OmniVideo-Test set, with notable gains across various tasks, especially in deep reasoning and long-term temporal understanding. The models also show strong generalization on benchmarks like Daily-Omni and JointAVBench, indicating the robustness of the proposed approach.
This research marks a significant step forward in multimodal AI, offering a scalable method to generate high-quality, evidence-grounded datasets that foster models capable of complex, long-term, and cross-modal reasoning. The structured scripts and evidence chains not only improve model performance but also enhance interpretability and factual grounding, paving the way for more intelligent and reliable video understanding systems. Despite current limitations in complex scene handling and noise in automated data, the framework sets a promising foundation for future advancements in multimodal reasoning, with potential applications spanning content analysis, human-computer interaction, and automated content moderation.
Deep Analysis
Background
The evolution of multimodal video understanding has seen significant progress with datasets like MSRVTT, ActivityNet, and TVQA, which primarily focus on short clips and localized events. Early models such as VideoBERT and Video-Language Pretraining laid the groundwork for joint visual and textual understanding. However, these approaches often lack the capacity for long-term reasoning and deep cross-modal integration, especially in complex, real-world scenarios. Recent efforts like EgoVQA and JavisInst-Und have attempted to incorporate longer videos and more sophisticated annotations, but they still rely heavily on manual labeling and short temporal spans. The advent of large-scale multimodal models like VideoGPT and Flamingo has pushed the boundary further, yet the creation of high-quality, scalable datasets that support deep reasoning remains a challenge. Existing datasets are limited in size, diversity, and the ability to support long-term causal inference, which are critical for real-world applications such as content moderation, autonomous systems, and intelligent assistants.
Core Problem
Current video QA datasets and models face several core issues. First, short clip segmentation leads to disjointed understanding, breaking the natural association between sounds and their sources. Second, independent descriptions across segments cause entity inconsistency, making it difficult for models to track objects or persons over time. Third, existing datasets lack the complexity needed for deep reasoning, especially for tasks requiring causal inference, temporal ordering, or multi-step logic. These limitations hinder the development of models capable of comprehensive scene understanding and robust reasoning in real-world applications. Moreover, manual annotation bottlenecks restrict dataset scale and diversity, impeding the training of truly generalizable models. Addressing these challenges requires a paradigm shift towards structured, scalable, and evidence-grounded data generation methods that can support long-term, cross-modal reasoning.
Innovation
The key innovations of this work include: 1) a structured scripting framework that transforms raw videos into entity-anchored, comprehensive scripts, ensuring entity consistency and source attribution across segments; 2) a clue mining strategy that leverages large models to extract multi-modal, multi-segment reasoning cues, forming complex causal and temporal chains; 3) an automated pipeline capable of generating 100,000 high-quality, evidence-grounded QA pairs, significantly reducing manual effort and increasing scalability; 4) multi-task fine-tuning techniques that improve model robustness across alignment, understanding, and reasoning tasks. These innovations collectively enable models to perform deep, long-term, and cross-modal reasoning, addressing the fundamental limitations of prior datasets and methods.
Methodology
- �� 视频预处理：利用多模态大模型识别主要实体，生成实体列表，作为全局指代基准。
- �� 脚本生成：将视频划分为主段（目标时长15秒），结合语音转录、视觉描述和非语音声音，生成结构化脚本，确保实体指代一致。
- �� 音频处理：提取语音转录，标注说话人，关联视觉实体，生成时间戳和描述，确保音源与视觉对应。
- �� 线索挖掘：利用脚本内容，采用大模型扫描多段、多模态信息，提取因果关系、事件链等线索，形成推理链。
- �� 问答生成：基于线索，模型聚焦关键段落，生成长时跨度、多模态依赖的问答对，确保答案 grounded in证据链。
- �� 数据集构建：自动化流程生成10万对训练样本，结合人工验证，确保质量。
- �� 模型微调：在不同预训练模型（如Qwen系列、VITA-1.5）上进行多任务微调，优化推理和理解能力。
Experiments
采用多源视频数据，涵盖10个音视频任务类别，数据来自网络平台，筛选高质量、多样性强的视频。训练过程中，使用不同模型（Qwen-2.5、VITA-1.5等）进行微调，设置合理超参数（如学习率、批次大小），并进行消融实验验证结构化脚本和线索挖掘的贡献。评估指标包括准确率、F1值、长时跨度推理准确性等。测试集由人工验证，确保问答的真实性和多模态依赖性。通过与未微调模型和其他数据集（如Daily-Omni、JointAVBench）对比，验证方法的有效性和泛化能力。
Results
微调后，Qwen3-Omni-30B模型在OmniVideo-Test上的整体性能提升13.86%，达到63.56%的准确率。细分任务中，Alignment任务提升至43.10%，Reasoning任务提升至45.04%。模型在长视频（>2分钟）中的表现优于短视频（<2分钟），验证了结构化脚本和线索引导的有效性。与基线模型相比，性能提升最大达20.59%，特别在深层推理和跨段实体指代方面表现出明显优势。定性分析显示，微调模型能更好地结合多模态线索，避免单模态推测，显著改善时间对齐和推理深度。
Applications
该技术可广泛应用于智能视频内容分析、自动问答、内容检索和视频编辑等场景。企业可以利用结构化脚本进行内容摘要和索引，提升内容管理效率。智能助手和机器人可以通过深度理解视频内容，实现更自然的人机交互。未来，结合实时线索挖掘和动态脚本更新，有望实现实时多模态场景理解，推动智能监控、教育和娱乐产业的变革。
Limitations & Outlook
尽管结构化脚本提升了跨段实体一致性，但在多主体、多角度复杂场景中仍存在识别误差。线索挖掘依赖预定义任务范畴，可能难以覆盖所有潜在推理路径。自动化数据生成虽规模庞大，但存在噪声和偏差，模型在极端或偏离训练分布的场景中表现尚需优化。未来需引入更强的多模态线索自适应机制和多任务学习策略，以增强模型的鲁棒性和泛化能力。
Plain Language Accessible to non-experts
想象你在一家大型工厂工作，这个工厂生产各种商品。每个工序都需要不同的机器和工人协作，有的工序需要看清楚每个零件，有的需要听到机器的声音。以前，工厂的管理系统只能看到每个工序的单独信息，比如只知道哪个机器在工作或哪个工人在操作，但不能理解整个生产流程的关联。现在，研究人员开发了一套新系统，就像给工厂装上了智能大脑，能把所有工序的内容整理成一份完整的生产报告，告诉你每个零件的来源、每个工序之间的关系以及整个生产的逻辑。这份报告不仅让工厂管理更清楚，也能帮助工人更好地合作，避免误工或重复工作。这个新系统用了一些聪明的技术，比如用“实体锚定”把每个零件和工人都标记清楚，用“线索挖掘”找到生产中的因果关系。它还能自动生成一份详细的生产问答，帮助工厂解决问题。通过这些技术，工厂变得更智能、更高效，也让我们更容易理解复杂的生产流程。
ELI14 Explained like you're 14
想象你在学校里参加一个大项目，里面有很多不同的环节，比如写报告、做实验、展示演讲。以前，如果老师只让你看每个环节的短视频，你可能只知道表面内容，比如谁在讲什么，或者实验做了什么，但不知道这些环节之间的关系，也不能理解为什么要这样做。现在，科学家们发明了一种新方法，就像给你准备了一份超级详细的项目指南，把每个环节都写成一份完整的故事，包括谁是主要人物、每个场景发生了什么、声音和画面是怎么配合的。这样，你就可以清楚地看到整个项目的流程，理解每个环节的原因和结果。这份指南还能帮你回答各种问题，比如“为什么要这样做？”或者“下一步会发生什么？”它用了一些聪明的技巧，比如给每个人物起名字，跟踪他们的行动，还能找到不同场景之间的因果关系。通过这个方法，你可以更好地理解复杂的事情，就像看一本精彩的故事书一样，既有趣又容易懂。
Glossary
Multimodal Large Language Model (多模态大语言模型)
一种结合视觉、听觉和文本信息的深度学习模型，能理解和生成多模态内容。在论文中，用于将视频内容转化为结构化脚本。
用于生成视频的结构化描述和问答对。
结构化脚本 (Structured Script)
一种将视频内容按照摘要、实体、段落描述等结构整理的文本格式，确保跨段实体一致性和信息完整性。
作为模型理解和推理的基础输入。
线索引导问答 (Clue-Guided QA)
通过挖掘多模态、多段线索，指导模型生成具有长时跨度和深层依赖的问答对。
提升模型的跨模态推理能力。
证据链 (Evidence Chain)
由多模态、多段线索组成的推理路径，支撑问答的事实依据。
用于确保问答的真实性和可解释性。
实体锚定 (Entity Anchoring)
在脚本中为主要实体赋予唯一标识，确保跨段实体指代一致。
实现实体在不同段落中的连续性。
多模态推理 (Multimodal Reasoning)
结合视觉、听觉和文本信息进行逻辑推理的能力。
模型在长时跨度、多模态场景中的核心能力。
自动化数据引擎 (Automated Data Engine)
利用模型自动生成大规模高质量训练数据的系统。
构建OmniVideo-100K数据集。
多任务微调 (Multi-task Fine-tuning)
在多个相关任务上同时训练模型，以增强其泛化能力。
提升模型在不同音视频任务中的表现。
长时跨度推理 (Long-term Temporal Reasoning)
理解和推断跨越多个时间段的事件关系。
模型能力的关键指标之一。
跨模态依赖 (Cross-modal Dependency)
不同模态信息之间的相互依赖关系。
模型理解复杂场景的基础。
Open Questions Unanswered questions from this research
- 1 尽管结构化脚本和线索挖掘显著提升了模型的推理能力，但在极端复杂场景（如多人互动、多源信息模糊）下，实体识别和线索提取仍存在误差。未来需要引入更强的多模态自适应线索挖掘技术，提升模型在多变环境中的鲁棒性。此外，自动生成数据虽规模庞大，但仍存在噪声和偏差，模型在极端或偏离训练分布的场景中表现尚需优化。
Applications
Immediate Applications
智能视频内容分析
利用结构化脚本和证据链技术，实现对视频内容的自动理解、摘要和问答，提升内容检索和内容管理效率。
自动问答系统
为智能助手和机器人提供更深层次的多模态理解能力，实现自然交互和场景理解。
内容审核与监控
通过深度推理识别视频中的复杂事件和潜在风险，增强安全监控能力。
Long-term Vision
智能视频场景理解
结合动态线索挖掘和实时脚本更新，实现对复杂场景的深度理解与推理，推动智能监控、教育和娱乐产业变革。
多模态人机交互
实现更自然、更智能的多模态交互系统，支持长时记忆和复杂推理，带来更丰富的人机体验。
Abstract
Current automated pipelines for audio-visual Question Answering (QA) generally adopt a ``video-caption-QA'' paradigm. However, these methods typically segment videos into short clips and generate separate descriptions for audio and visual modalities. This decoupled processing severs inherent associations between sounds and their visual sources, while independent clip processing often causes inconsistent descriptions of the same entity across segments. Furthermore, coupling long-text comprehension and QA synthesis into a single step often restricts models to localized events, yielding questions lacking long-term temporal connections and deep cross-modal reasoning. To address these issues, we propose an automated data engine featuring two mechanisms: (1) \textbf{Entity-Anchored Video Scripting} transforms videos into structured scripts, comprising summaries, main entity lists, and segment-wise audio-visual descriptions. The entity list serves as a global prior to ensure cross-segment referential consistency and reconstruct audio-visual associations. (2) \textbf{Clue-Guided QA Generation} prompts models to first mine cross-segment, multimodal clues from the script, and subsequently generate QA pairs based on these high-value clues. Leveraging this pipeline, we construct the instruction-tuning dataset \textbf{OmniVideo-100K} and a human-verified test set, \textbf{OmniVideo-Test}. Fine-tuning VITA-1.5, Qwen2.5-Omni-7B and Qwen3-Omni-30B on OmniVideo-100K yields performance gains of up to 20.59% on OmniVideo-Test, demonstrating strong generalization (up to 12.64% improvements) across established benchmarks like Daily-Omni and JointAVBench.
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