🤖 AI Summary
This work addresses fundamental challenges in multimodal AI systems—namely, the absence of a unified chunking framework, cross-modal semantic inconsistency, and asynchronous information density coupled with noise interference. We propose the first comprehensive, modality-agnostic chunking taxonomy covering text, images, audio, video, and cross-modal data. Methodologically, we integrate fixed-size windowing, recursive text splitting, object-level visual chunking, silence-aware audio segmentation, and scene-aware video segmentation, implemented via a reusable technical framework leveraging LangChain, Detectron2, and PySceneDetect. Our contributions include: (1) a systematic characterization of the granularity–context trade-off; (2) a novel cross-modal chunking mechanism preserving semantic consistency; (3) identification and formal modeling of asynchronous information density and alignment noise as open problems; and (4) foundational theoretical and practical groundwork for adaptive, learning-driven, and task-specific chunking methodologies.
📝 Abstract
Our goal is to consolidate 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.