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.

翻译：分块已成为一种关键技术，通过将生成模型的响应建立在高效分割的知识基础上，从而增强其性能[1]。虽然该技术最初为单模态（主要是文本）领域开发，但多模态基础模型的最新进展已将分块方法扩展到包含多种数据类型，包括图像、音频和视频[2]。这些系统成功的关键支撑在于分块策略——即如何将大规模、连续的多模态数据流分割成适合处理的语义单元[3]。尽管分块策略至关重要，但它仍是一个尚未充分探索的领域，尤其是在多模态系统中，特定模态的约束、语义保持以及跨模态对齐带来了独特的挑战。我们的目标是整合多模态分块策略的研究现状，为研究人员和从业者提供技术基础和设计空间，以开发更有效、更高效的多模态人工智能系统。本综述为构建鲁棒的分块流程铺平了道路，这些流程能够随模态复杂性扩展，提高处理准确性，并增强实际应用中的生成连贯性。本综述针对每种模态（文本、图像、音频、视频和跨模态数据）提供了全面的分块策略分类和技术分析。我们研究了经典和现代方法，例如固定大小的令牌窗口、递归文本分割、以对象为中心的视觉分块、基于静默的音频分割以及视频中的场景检测。每种方法都从其基本方法论、支持工具（例如LangChain、Detectron2、PySceneDetect）、优势以及挑战（特别是与粒度-上下文权衡和多模态对齐相关的挑战）方面进行了分析。此外，我们探讨了新兴的跨模态分块策略，这些策略旨在保持不同数据类型之间的对齐和语义一致性[4]。我们还提供了比较性见解，强调了诸如异步信息密度和噪声对齐信号等开放性问题，并指出了未来在自适应、基于学习和任务特定分块方面的研究机会。