This paper presents OmniDataComposer, an innovative approach for multimodal data fusion and unlimited data generation with an intent to refine and uncomplicate interplay among diverse data modalities. Coming to the core breakthrough, it introduces a cohesive data structure proficient in processing and merging multimodal data inputs, which include video, audio, and text. Our crafted algorithm leverages advancements across multiple operations such as video/image caption extraction, dense caption extraction, Automatic Speech Recognition (ASR), Optical Character Recognition (OCR), Recognize Anything Model(RAM), and object tracking. OmniDataComposer is capable of identifying over 6400 categories of objects, substantially broadening the spectrum of visual information. It amalgamates these diverse modalities, promoting reciprocal enhancement among modalities and facilitating cross-modal data correction. \textbf{The final output metamorphoses each video input into an elaborate sequential document}, virtually transmuting videos into thorough narratives, making them easier to be processed by large language models. Future prospects include optimizing datasets for each modality to encourage unlimited data generation. This robust base will offer priceless insights to models like ChatGPT, enabling them to create higher quality datasets for video captioning and easing question-answering tasks based on video content. OmniDataComposer inaugurates a new stage in multimodal learning, imparting enormous potential for augmenting AI's understanding and generation of complex, real-world data.

翻译：本文提出了OmniDataComposer，一种面向多模态数据融合与无限数据生成的创新方法，旨在精简并简化不同数据模态间的交互。其核心突破在于引入了一个统一的、能够高效处理与融合多模态数据输入（包括视频、音频和文本）的数据结构。所设计的算法融合了多项先进操作，如视频/图像字幕提取、密集字幕提取、自动语音识别（ASR）、光学字符识别（OCR）、万物识别模型（RAM）以及目标跟踪。OmniDataComposer能够识别超过6400种对象类别，显著拓宽了视觉信息的频谱。它融合了这些多样化的模态，促进了模态间的相互增强，并实现了跨模态数据校正。**最终输出将每个视频输入转化为一份详尽的顺序文档**，实质上将视频转变成完整的叙述文本，使其更易于被大语言模型处理。未来展望包括为每种模态优化数据集，以支持无限数据生成。这一坚实基础将为ChatGPT等模型提供宝贵见解，使其能够为视频字幕生成创建更高质量的数据集，并简化基于视频内容的问答任务。OmniDataComposer开创了多模态学习的新阶段，为增强人工智能对复杂真实世界数据的理解与生成赋予了巨大潜力。