系统梳理多模态数据分块策略，提升生成模型的准确性与一致性。
Chunking Strategies for Multimodal AI Systems
- 按模态分类分析文本、图像、音频、视频等分块方法
- 对比固定窗口、语义分割等策略在不同数据上的效果差异
- 适合多模态系统设计者与研究者参考，尤其关注跨模态对齐
分块技术已成为提升生成模型性能的关键，通过将知识高效分割以支持响应生成。最初用于单模态（主要是文本）领域，近年来随着多模态基础模型的发展，分块方法已扩展至图像、音频和视频等多种数据类型。系统成功的核心在于分块策略——如何将连续的多模态数据流划分为语义有意义的处理单元。尽管重要，该领域在多模态背景下仍缺乏深入研究，面临模态特异性约束、语义保持及跨模态对齐等挑战。本文综述了针对文本、图像、音频、视频及跨模态数据的分块策略，涵盖固定大小标记窗口、递归文本切分、基于对象的视觉分块、基于静音的音频分割、视频场景检测等方法。分析其原理、工具支持（如LangChain、Detectron2、PySceneDetect）、优势与局限，重点讨论粒度与上下文权衡、跨模态对齐问题。还探讨新兴的跨模态分块策略，强调对齐与语义一致性保持，并提出开放问题，如异步信息密度、噪声对齐信号，以及自适应、学习型、任务驱动分块的研究机遇。
原文摘要 · Abstract (English)
Chunking has emerged as a critical technique that enhances generative models by grounding their responses in efficiently segmented knowledge [1]. While initially developed for unimodal (primarily textual) domains, recent advances in multimodal foundation models have extended chunking approaches to incorporate diverse data types, including images, audio, and video [2]. A critical component underpinning the success of these systems is the chunking strategy how large, continuous streams of multimodal data are segmented into semantically meaningful units suitable for processing [3]. Despite its importance, chunking remains an under-explored area, especially in the context of multimodal systems where modality-specific constraints, semantic preservation, and alignment across modalities introduce unique challenges. Our goal is to consolidating the landscape of multimodal chunking strategies, providing researchers and practitioners with a technical foundation and design space for developing more effective and efficient multimodal AI systems. This survey paves the way for innovations in robust chunking pipelines that scale with modality complexity, enhance processing accuracy, and improve generative coherence in real-world applications. This survey provides a comprehensive taxonomy and technical analysis of chunking strategies tailored for each modality: text, images, audio, video, and cross-modal data. We examine classical and modern approaches such as fixed-size token windowing, recursive text splitting, object-centric visual chunking, silence-based audio segmentation, and scene detection in videos. Each approach is analyzed in terms of its underlying methodology, supporting tools (e.g., LangChain, Detectron2, PySceneDetect), benefits, and challenges, particularly those related to granularity-context trade-offs and multimodal alignment. Furthermore, we explore emerging cross-modal chunking strategies that aim to preserve alignment and semantic consistency across disparate data types [4]. We also include comparative insights, highlight open problems such as asynchronous information density and noisy alignment signals, and identify opportunities for future research in adaptive, learning-based, and task-specific chunking.
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