DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data

TL;DR

This paper introduces DSDL, a unified data description language that standardizes diverse AI datasets, simplifying data processing, dissemination, and extension across different modalities and tasks.

Contribution

The paper presents DSDL, a novel framework that standardizes AI dataset descriptions, enabling easier data sharing, processing, and extension for various modalities and tasks.

Findings

DSDL reduces dataset processing workload.

Predefined templates facilitate dataset conversion.

Tools and documentation improve user experience.

Abstract

In the era of artificial intelligence, the diversity of data modalities and annotation formats often renders data unusable directly, requiring understanding and format conversion before it can be used by researchers or developers with different needs. To tackle this problem, this article introduces a framework called Dataset Description Language (DSDL) that aims to simplify dataset processing by providing a unified standard for AI datasets. DSDL adheres to the three basic practical principles of generic, portable, and extensible, using a unified standard to express data of different modalities and structures, facilitating the dissemination of AI data, and easily extending to new modalities and tasks. The standardized specifications of DSDL reduce the workload for users in data dissemination, processing, and usage. To further improve user convenience, we provide predefined DSDL templates for various tasks, convert mainstream datasets to comply with DSDL specifications, and provide comprehensive documentation and DSDL tools. These efforts aim to simplify the use of AI data, thereby improving the efficiency of AI development.