VDCook:DIY video data cook your MLLMs
arXiv:2603.05539v1 Announce Type: cross Abstract: We introduce VDCook: a self-evolving video data operating system, a configurable video data construction platform for researchers and vertical domain teams. Users initiate data requests via natural language queries and adjustable parameters (scale, retrieval-synthesis ratio, quality threshold). The system automatically performs query optimization, concurrently running real video retrieval and controlled synthesis modules. It ultimately generates in-domain data packages with complete provenance and metadata, along with reproducible Notebooks. Unlike traditional static, one-time-built datasets, VDCook enables continuous updates and domain expansion through its automated data ingestion mechanism based on MCP (Model Context Protocol)\cite{mcp2024anthropic}, transforming datasets into dynamically evolving open ecosystems. The system also provides multi-dimensional metadata annotation (scene segmentation, motion scoring, OCR ratio, automat
arXiv:2603.05539v1 Announce Type: cross Abstract: We introduce VDCook: a self-evolving video data operating system, a configurable video data construction platform for researchers and vertical domain teams. Users initiate data requests via natural language queries and adjustable parameters (scale, retrieval-synthesis ratio, quality threshold). The system automatically performs query optimization, concurrently running real video retrieval and controlled synthesis modules. It ultimately generates in-domain data packages with complete provenance and metadata, along with reproducible Notebooks. Unlike traditional static, one-time-built datasets, VDCook enables continuous updates and domain expansion through its automated data ingestion mechanism based on MCP (Model Context Protocol)\cite{mcp2024anthropic}, transforming datasets into dynamically evolving open ecosystems. The system also provides multi-dimensional metadata annotation (scene segmentation, motion scoring, OCR ratio, automatic captioning, etc.), laying the foundation for flexible subsequent data `cooking' and indexing\cite{vlogger}. This platform aims to significantly lower the barrier to constructing specialized video training datasets through infrastructure-level solutions, while supporting community contributions and a governance-enabled data expansion paradigm. \textbf{Project demo:} https://screenapp.io/app/v/WP0SvffgsH
Executive Summary
The article introduces VDCook, a self-evolving video data operating system that enables researchers and domain teams to construct and manage video training datasets. VDCook allows users to initiate data requests via natural language queries and adjustable parameters, and the system automatically generates in-domain data packages with complete provenance and metadata. The platform aims to lower the barrier to constructing specialized video training datasets and supports community contributions and governance-enabled data expansion.
Key Points
- ▸ VDCook is a self-evolving video data operating system
- ▸ The system enables users to initiate data requests via natural language queries and adjustable parameters
- ▸ VDCook generates in-domain data packages with complete provenance and metadata
Merits
Flexibility and Customization
VDCook allows users to customize their data requests and parameters, enabling the creation of specialized video training datasets tailored to specific needs
Automated Data Ingestion
The system's automated data ingestion mechanism based on Model Context Protocol (MCP) enables continuous updates and domain expansion
Demerits
Complexity and Scalability
The system's complexity and scalability may pose challenges for users without extensive technical expertise or large-scale computational resources
Expert Commentary
The introduction of VDCook represents a significant advancement in the field of video data construction and management. The system's flexibility, customization options, and automated data ingestion mechanism make it an attractive solution for researchers and domain teams. However, the complexity and scalability of the system may pose challenges for some users, and the governance-enabled data expansion paradigm raises important questions about data ownership, privacy, and ethics. As the use of VDCook and similar systems becomes more widespread, it is essential to develop and implement policies and regulations that ensure the responsible use of AI training datasets and protect user privacy.
Recommendations
- ✓ Further research is needed to address the complexity and scalability challenges associated with VDCook
- ✓ The development of clear policies and regulations is necessary to ensure the responsible use of VDCook and similar systems
