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

Bin Wang, Linke Ouyang, Fan Wu, Wenchang Ning, Xiao Han, Zhiyuan Zhao, Jiahui Peng, Yiying Jiang, Dahua Lin, Conghui He·May 28, 2024

Summary

DSDL (Dataset Description Language) is a framework designed to simplify AI dataset management by providing a unified standard for expressing diverse data modalities and structures. It adheres to principles of generality, portability, and extensibility, facilitating data dissemination, processing, and usage across different environments. DSDL includes standardized specifications, templates for image analysis tasks like classification, object detection, and OCR, converted mainstream datasets, and accompanying tools and documentation. The language uses JSON or YAML for description, with a focus on organizing data, handling unstructured objects, and defining parametric classes for flexibility. It supports various tasks, such as image classification, object detection, and segmentation, with standardized fields and examples for easy application. DSDL aims to streamline AI data utilization, enhance development efficiency, and reduce the complexity of managing complex datasets.

Key findings

53

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper focuses on introducing the Data Set Description Language (DSDL) to bridge modalities and tasks in AI data. It aims to provide a standardized way to describe data sets, including defining data structures, basic types, class labels, object locators, and struct classes . This paper addresses the challenge of efficiently describing complex data sets used in artificial intelligence applications, ensuring interoperability and ease of interpretation across different data types and domains . While the concept of describing data sets is not new, the specific approach of using DSDL to address the complexities of AI data sets and tasks can be considered a novel contribution in the field of data description languages .


What scientific hypothesis does this paper seek to validate?

This paper focuses on the Data Set Description Language (DSDL) and aims to validate the hypothesis that by using DSDL, it is possible to bridge modalities and tasks in AI data effectively . The primary goal is to demonstrate how DSDL can serve as a language for describing data sets in a meaningful manner by endowing elements with semantics, thereby facilitating the interpretation, validation, and manipulation of data sets . The paper explores the core architecture of DSDL, which is based on JSON or YAML, and emphasizes the importance of leveraging existing tools for these languages to build comprehensive systems that support data set operations and interoperability within the AI ecosystem .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper on Data Set Description Language (DSDL) proposes several innovative ideas, methods, and models for bridging modalities and tasks in AI data . Here are some key points from the paper:

  1. DSDL Specification: The paper introduces the DSDL Specification, which defines a structured way to describe data sets for AI tasks. It includes sections on basic types, generic basic types like Bool, Int, Num, and Str, as well as special basic types like Coord, Coord3D, Interval, BBox, Polygon, Date, and Time .

  2. Label and Object Locator Types: DSDL introduces the concept of class labels represented as strings with type Label, allowing for semantic classification of objects. It also defines object locators as references to unstructured objects, such as images, videos, and texts, represented by specially-formatted strings .

  3. Struct Classes: The paper highlights the use of struct classes to represent composite entities in data sets. Structs are used to encapsulate multiple elements of a data sample, making them suitable for representing complex data structures .

  4. Data Section Definition: The paper outlines the structure of the data section in DSDL, which includes modules for defining sample types, sample paths, and storing sample data of the dataset. It emphasizes the importance of defining the data type and path for samples to effectively manage and interpret the dataset .

  5. Libraries: To simplify the process of data set description, the paper introduces the concept of libraries in DSDL. Libraries contain definitions that can be imported into data set description files, reducing the need to redefine common structures. This approach enhances reusability and simplifies the overall data description process .

  6. Image Segmentation Sample: The paper presents an example of an Image Segmentation Sample class definition, showcasing how DSDL can be used to define complex data structures like image samples with associated label maps. This demonstrates the flexibility of DSDL in handling diverse data types and structures .

  7. Core Architecture: The paper emphasizes that DSDL is a domain-specific language based on JSON and YAML, enabling structured description of data sets while separating them from unstructured media content. This design choice allows for efficient data manipulation, distribution, and interoperability with existing tool systems . The Data Set Description Language (DSDL) introduces several characteristics and advantages compared to previous methods, as outlined in the paper:

  8. Structured Data Description: DSDL provides a structured and standardized way to describe data sets for AI tasks, bridging modalities and tasks effectively . It defines a clear specification for data representation, including basic types, struct classes, and data section definitions, enhancing the organization and interpretation of diverse data types within a dataset.

  9. Flexible Data Representation: DSDL allows for flexible data representation by defining basic types such as Bool, Int, Num, and Str, as well as special basic types like Coord, Coord3D, Interval, BBox, Polygon, Date, and Time . This flexibility enables the representation of various data elements, from simple values to complex structures like coordinates, intervals, and polygons, enhancing the versatility of data description.

  10. Parameterized Types: DSDL supports parameterized types, allowing for customization of how elements are expressed, interpreted, and validated . For instance, Label type accepts parameters like class domain, enabling the specification of the context for classification labels. This parameterization enhances the adaptability of data types to specific requirements and domains.

  11. Unified Class Labeling: DSDL introduces the concept of class domains to represent different contexts for classification, providing a standardized approach to class labeling . By allowing labels to be expressed in name-based or index-based formats within specific domains, DSDL promotes consistency and clarity in assigning semantic meaning to objects across different classification contexts.

  12. Efficient Data Management: The use of libraries in DSDL simplifies data set description by enabling the reuse of common definitions . By defining global types and structures in libraries that can be imported into description files, DSDL streamlines the data description process, reducing redundancy and enhancing the efficiency of data management tasks.

  13. Interoperability and Compatibility: DSDL is based on JSON and YAML formats, ensuring compatibility with existing tool systems and facilitating interoperability with different platforms . This design choice allows for seamless integration of DSDL-described datasets into various AI applications and workflows, promoting data sharing and collaboration in AI research and development.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field related to the Data Set Description Language (DSDL), there are notable researchers who have contributed to this topic. The paper mentions researchers Bin et al. who have worked on the DSDL specification . The key solution mentioned in the paper is the introduction of object locators, which separate the structured description of the dataset from the unstructured media content. This approach enables the lightweight distribution of dataset descriptions without moving large media data volumes and allows for quick dataset manipulation, such as combining sets or taking subsets .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific goals in mind, focusing on three key aspects: generic, portable, and extensible .

  • Generic: The design aimed to provide a unified representation standard for data in various fields of artificial intelligence, rather than being tailored to a single field or task. This allowed for the expression of datasets with different modalities and structures in a consistent format .
  • Portable: The goal was to create a system where dataset descriptions could be easily distributed and exchanged without the need for modifications in different environments. This ensured that dataset descriptions could be widely used across different platforms and settings .
  • Extensible: The design allowed for the extension of the expression boundaries without altering the core standard. This flexibility enabled the language to evolve and adapt to new requirements over time, similar to how libraries and packages extend the functionality of programming languages like C++ or Python .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the context of the Data Set Description Language (DSDL) is not explicitly mentioned in the provided excerpts. The DSDL focuses on describing data structures, types, and formats for AI data . Regarding the open-source status of the code related to DSDL, the information about the code being open source is not provided in the given context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The paper references various experiments conducted in the field of artificial intelligence, such as image recognition, object tracking, and pose estimation, among others . These experiments are based on datasets like Cityscapes, Fashion-mnist, and Open Images, which serve as benchmarks for evaluating machine learning algorithms and models . The results obtained from these experiments contribute to advancing technologies and applications in AI by providing insights into the performance and capabilities of different models and algorithms . The use of diverse datasets and benchmarks in the experiments enhances the credibility and generalizability of the findings, supporting the scientific hypotheses under investigation .


What are the contributions of this paper?

The paper on "DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data" makes several contributions in the field of data set description language:

  • Definition of Basic Types: The paper defines generic basic types in DSDL, including Bool (boolean type), Int (integer type), Num (general numeric type), and Str (string type) .
  • Special Basic Types: It introduces special basic types like Coord, Coord3D, Interval, BBox, Polygon, Date, and Time, each with specific semantics and interpretations .
  • Label and Object Locator Types: The paper defines Label as a class label type and Loc as an object locator type, providing detailed explanations on how they are used and represented in DSDL .
  • Struct Classes: It explains the concept of struct classes in DSDL, which are used to represent composite entities and data samples, allowing for the abstraction of complex data structures .
  • Data Section Definition: The paper outlines how the data section in DSDL is structured, including defining sample types, sample paths, and storing sample data within a dataset .
  • Usage of Libraries: It introduces the concept of libraries in DSDL to simplify the process of defining classes and importing them into data set descriptions, enhancing the reusability and modularity of data descriptions .
  • Core Architecture: The paper highlights the core architecture of DSDL, emphasizing its domain-specific nature based on JSON or YAML, enabling meaningful description of data sets and leveraging existing tools for interpretation, validation, and interoperability .

What work can be continued in depth?

To delve deeper into the Data Set Description Language (DSDL) and continue working on its development, several areas can be explored further :

  • Simplifying Class Definitions: Efforts can be made to simplify the process of defining classes in DSDL to make it more user-friendly for AI researchers and developers.
  • Libraries Integration: Further exploration can focus on defining and importing libraries in DSDL to streamline the data set description process. Libraries can help in reusing common definitions across different data sets.
  • Enhancing Data Section Handling: Research can be conducted on optimizing the handling of the data section in DSDL, especially when dealing with large datasets. This can involve strategies for efficiently extracting and storing data from YAML files.
  • Extending Type Parameters Usage: Exploring the usage of type parameters in DSDL, such as customizing Date and Time types with specific formats, can be an area for further development.
  • Struct Classes Enhancement: Further enhancements can be made in struct classes to represent composite entities more effectively, especially when dealing with complex data structures like object detection samples with nested structs.
  • Object Locators Implementation: Delving into the implementation and utilization of object locators in DSDL can be beneficial. Object locators play a crucial role in separating structured data set descriptions from unstructured media content, enabling efficient data manipulation and distribution.

By focusing on these areas, the development and refinement of DSDL can be extended to enhance its usability, flexibility, and efficiency in describing diverse AI data sets .

Tables

8

Introduction
Background
Motivation: The need for a unified standard in AI dataset management
Challenges: Current limitations in managing diverse data modalities
Objective
Primary goal: To provide a standardized language for dataset description
Secondary goals: Generality, portability, and extensibility
Method
Data Description and Organization
Standardized Language: JSON or YAML for easy readability
Data Structure: Parametric classes and unstructured object handling
DSDL Components
Image Analysis Templates
Image classification
Object detection
Optical Character Recognition (OCR)
Mainstream Dataset Conversions
Compatibility with popular datasets
Field Specifications
Standardized fields for consistent data representation
Examples and Use Cases
Demonstrating how to apply DSDL in various tasks
Data Collection and Preprocessing
Data Collection Methods: Adapting to different data sources
Data Preprocessing Techniques
Handling data cleaning, normalization, and augmentation
Tools and Documentation
DSDL Tools: Software for creating, validating, and sharing datasets
User Guide and Tutorials: Resources for developers and dataset creators
Benefits and Impact
Efficiency Improvement: Streamlining AI development process
Complexity Reduction: Simplifying management of complex datasets
Interoperability: Facilitating data sharing and collaboration across platforms
Basic info
papers
programming languages
artificial intelligence
Advanced features
Insights
Which programming languages does DSDL support for data description?
What are some key features of DSDL that make it suitable for diverse data modalities?
How does DSDL address the challenges in AI dataset management?
What is the primary purpose of DSDL?

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

Bin Wang, Linke Ouyang, Fan Wu, Wenchang Ning, Xiao Han, Zhiyuan Zhao, Jiahui Peng, Yiying Jiang, Dahua Lin, Conghui He·May 28, 2024

Summary

DSDL (Dataset Description Language) is a framework designed to simplify AI dataset management by providing a unified standard for expressing diverse data modalities and structures. It adheres to principles of generality, portability, and extensibility, facilitating data dissemination, processing, and usage across different environments. DSDL includes standardized specifications, templates for image analysis tasks like classification, object detection, and OCR, converted mainstream datasets, and accompanying tools and documentation. The language uses JSON or YAML for description, with a focus on organizing data, handling unstructured objects, and defining parametric classes for flexibility. It supports various tasks, such as image classification, object detection, and segmentation, with standardized fields and examples for easy application. DSDL aims to streamline AI data utilization, enhance development efficiency, and reduce the complexity of managing complex datasets.
Mind map
Data Preprocessing Techniques
Data Collection Methods: Adapting to different data sources
Optical Character Recognition (OCR)
Object detection
Image classification
Interoperability: Facilitating data sharing and collaboration across platforms
Complexity Reduction: Simplifying management of complex datasets
Efficiency Improvement: Streamlining AI development process
User Guide and Tutorials: Resources for developers and dataset creators
DSDL Tools: Software for creating, validating, and sharing datasets
Handling data cleaning, normalization, and augmentation
Demonstrating how to apply DSDL in various tasks
Examples and Use Cases
Standardized fields for consistent data representation
Field Specifications
Compatibility with popular datasets
Mainstream Dataset Conversions
Image Analysis Templates
Data Structure: Parametric classes and unstructured object handling
Standardized Language: JSON or YAML for easy readability
Secondary goals: Generality, portability, and extensibility
Primary goal: To provide a standardized language for dataset description
Challenges: Current limitations in managing diverse data modalities
Motivation: The need for a unified standard in AI dataset management
Benefits and Impact
Tools and Documentation
Data Collection and Preprocessing
DSDL Components
Data Description and Organization
Objective
Background
Method
Introduction
Outline
Introduction
Background
Motivation: The need for a unified standard in AI dataset management
Challenges: Current limitations in managing diverse data modalities
Objective
Primary goal: To provide a standardized language for dataset description
Secondary goals: Generality, portability, and extensibility
Method
Data Description and Organization
Standardized Language: JSON or YAML for easy readability
Data Structure: Parametric classes and unstructured object handling
DSDL Components
Image Analysis Templates
Image classification
Object detection
Optical Character Recognition (OCR)
Mainstream Dataset Conversions
Compatibility with popular datasets
Field Specifications
Standardized fields for consistent data representation
Examples and Use Cases
Demonstrating how to apply DSDL in various tasks
Data Collection and Preprocessing
Data Collection Methods: Adapting to different data sources
Data Preprocessing Techniques
Handling data cleaning, normalization, and augmentation
Tools and Documentation
DSDL Tools: Software for creating, validating, and sharing datasets
User Guide and Tutorials: Resources for developers and dataset creators
Benefits and Impact
Efficiency Improvement: Streamlining AI development process
Complexity Reduction: Simplifying management of complex datasets
Interoperability: Facilitating data sharing and collaboration across platforms
Key findings
53

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper focuses on introducing the Data Set Description Language (DSDL) to bridge modalities and tasks in AI data. It aims to provide a standardized way to describe data sets, including defining data structures, basic types, class labels, object locators, and struct classes . This paper addresses the challenge of efficiently describing complex data sets used in artificial intelligence applications, ensuring interoperability and ease of interpretation across different data types and domains . While the concept of describing data sets is not new, the specific approach of using DSDL to address the complexities of AI data sets and tasks can be considered a novel contribution in the field of data description languages .


What scientific hypothesis does this paper seek to validate?

This paper focuses on the Data Set Description Language (DSDL) and aims to validate the hypothesis that by using DSDL, it is possible to bridge modalities and tasks in AI data effectively . The primary goal is to demonstrate how DSDL can serve as a language for describing data sets in a meaningful manner by endowing elements with semantics, thereby facilitating the interpretation, validation, and manipulation of data sets . The paper explores the core architecture of DSDL, which is based on JSON or YAML, and emphasizes the importance of leveraging existing tools for these languages to build comprehensive systems that support data set operations and interoperability within the AI ecosystem .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper on Data Set Description Language (DSDL) proposes several innovative ideas, methods, and models for bridging modalities and tasks in AI data . Here are some key points from the paper:

  1. DSDL Specification: The paper introduces the DSDL Specification, which defines a structured way to describe data sets for AI tasks. It includes sections on basic types, generic basic types like Bool, Int, Num, and Str, as well as special basic types like Coord, Coord3D, Interval, BBox, Polygon, Date, and Time .

  2. Label and Object Locator Types: DSDL introduces the concept of class labels represented as strings with type Label, allowing for semantic classification of objects. It also defines object locators as references to unstructured objects, such as images, videos, and texts, represented by specially-formatted strings .

  3. Struct Classes: The paper highlights the use of struct classes to represent composite entities in data sets. Structs are used to encapsulate multiple elements of a data sample, making them suitable for representing complex data structures .

  4. Data Section Definition: The paper outlines the structure of the data section in DSDL, which includes modules for defining sample types, sample paths, and storing sample data of the dataset. It emphasizes the importance of defining the data type and path for samples to effectively manage and interpret the dataset .

  5. Libraries: To simplify the process of data set description, the paper introduces the concept of libraries in DSDL. Libraries contain definitions that can be imported into data set description files, reducing the need to redefine common structures. This approach enhances reusability and simplifies the overall data description process .

  6. Image Segmentation Sample: The paper presents an example of an Image Segmentation Sample class definition, showcasing how DSDL can be used to define complex data structures like image samples with associated label maps. This demonstrates the flexibility of DSDL in handling diverse data types and structures .

  7. Core Architecture: The paper emphasizes that DSDL is a domain-specific language based on JSON and YAML, enabling structured description of data sets while separating them from unstructured media content. This design choice allows for efficient data manipulation, distribution, and interoperability with existing tool systems . The Data Set Description Language (DSDL) introduces several characteristics and advantages compared to previous methods, as outlined in the paper:

  8. Structured Data Description: DSDL provides a structured and standardized way to describe data sets for AI tasks, bridging modalities and tasks effectively . It defines a clear specification for data representation, including basic types, struct classes, and data section definitions, enhancing the organization and interpretation of diverse data types within a dataset.

  9. Flexible Data Representation: DSDL allows for flexible data representation by defining basic types such as Bool, Int, Num, and Str, as well as special basic types like Coord, Coord3D, Interval, BBox, Polygon, Date, and Time . This flexibility enables the representation of various data elements, from simple values to complex structures like coordinates, intervals, and polygons, enhancing the versatility of data description.

  10. Parameterized Types: DSDL supports parameterized types, allowing for customization of how elements are expressed, interpreted, and validated . For instance, Label type accepts parameters like class domain, enabling the specification of the context for classification labels. This parameterization enhances the adaptability of data types to specific requirements and domains.

  11. Unified Class Labeling: DSDL introduces the concept of class domains to represent different contexts for classification, providing a standardized approach to class labeling . By allowing labels to be expressed in name-based or index-based formats within specific domains, DSDL promotes consistency and clarity in assigning semantic meaning to objects across different classification contexts.

  12. Efficient Data Management: The use of libraries in DSDL simplifies data set description by enabling the reuse of common definitions . By defining global types and structures in libraries that can be imported into description files, DSDL streamlines the data description process, reducing redundancy and enhancing the efficiency of data management tasks.

  13. Interoperability and Compatibility: DSDL is based on JSON and YAML formats, ensuring compatibility with existing tool systems and facilitating interoperability with different platforms . This design choice allows for seamless integration of DSDL-described datasets into various AI applications and workflows, promoting data sharing and collaboration in AI research and development.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field related to the Data Set Description Language (DSDL), there are notable researchers who have contributed to this topic. The paper mentions researchers Bin et al. who have worked on the DSDL specification . The key solution mentioned in the paper is the introduction of object locators, which separate the structured description of the dataset from the unstructured media content. This approach enables the lightweight distribution of dataset descriptions without moving large media data volumes and allows for quick dataset manipulation, such as combining sets or taking subsets .


How were the experiments in the paper designed?

The experiments in the paper were designed with specific goals in mind, focusing on three key aspects: generic, portable, and extensible .

  • Generic: The design aimed to provide a unified representation standard for data in various fields of artificial intelligence, rather than being tailored to a single field or task. This allowed for the expression of datasets with different modalities and structures in a consistent format .
  • Portable: The goal was to create a system where dataset descriptions could be easily distributed and exchanged without the need for modifications in different environments. This ensured that dataset descriptions could be widely used across different platforms and settings .
  • Extensible: The design allowed for the extension of the expression boundaries without altering the core standard. This flexibility enabled the language to evolve and adapt to new requirements over time, similar to how libraries and packages extend the functionality of programming languages like C++ or Python .

What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the context of the Data Set Description Language (DSDL) is not explicitly mentioned in the provided excerpts. The DSDL focuses on describing data structures, types, and formats for AI data . Regarding the open-source status of the code related to DSDL, the information about the code being open source is not provided in the given context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The paper references various experiments conducted in the field of artificial intelligence, such as image recognition, object tracking, and pose estimation, among others . These experiments are based on datasets like Cityscapes, Fashion-mnist, and Open Images, which serve as benchmarks for evaluating machine learning algorithms and models . The results obtained from these experiments contribute to advancing technologies and applications in AI by providing insights into the performance and capabilities of different models and algorithms . The use of diverse datasets and benchmarks in the experiments enhances the credibility and generalizability of the findings, supporting the scientific hypotheses under investigation .


What are the contributions of this paper?

The paper on "DSDL: Data Set Description Language for Bridging Modalities and Tasks in AI Data" makes several contributions in the field of data set description language:

  • Definition of Basic Types: The paper defines generic basic types in DSDL, including Bool (boolean type), Int (integer type), Num (general numeric type), and Str (string type) .
  • Special Basic Types: It introduces special basic types like Coord, Coord3D, Interval, BBox, Polygon, Date, and Time, each with specific semantics and interpretations .
  • Label and Object Locator Types: The paper defines Label as a class label type and Loc as an object locator type, providing detailed explanations on how they are used and represented in DSDL .
  • Struct Classes: It explains the concept of struct classes in DSDL, which are used to represent composite entities and data samples, allowing for the abstraction of complex data structures .
  • Data Section Definition: The paper outlines how the data section in DSDL is structured, including defining sample types, sample paths, and storing sample data within a dataset .
  • Usage of Libraries: It introduces the concept of libraries in DSDL to simplify the process of defining classes and importing them into data set descriptions, enhancing the reusability and modularity of data descriptions .
  • Core Architecture: The paper highlights the core architecture of DSDL, emphasizing its domain-specific nature based on JSON or YAML, enabling meaningful description of data sets and leveraging existing tools for interpretation, validation, and interoperability .

What work can be continued in depth?

To delve deeper into the Data Set Description Language (DSDL) and continue working on its development, several areas can be explored further :

  • Simplifying Class Definitions: Efforts can be made to simplify the process of defining classes in DSDL to make it more user-friendly for AI researchers and developers.
  • Libraries Integration: Further exploration can focus on defining and importing libraries in DSDL to streamline the data set description process. Libraries can help in reusing common definitions across different data sets.
  • Enhancing Data Section Handling: Research can be conducted on optimizing the handling of the data section in DSDL, especially when dealing with large datasets. This can involve strategies for efficiently extracting and storing data from YAML files.
  • Extending Type Parameters Usage: Exploring the usage of type parameters in DSDL, such as customizing Date and Time types with specific formats, can be an area for further development.
  • Struct Classes Enhancement: Further enhancements can be made in struct classes to represent composite entities more effectively, especially when dealing with complex data structures like object detection samples with nested structs.
  • Object Locators Implementation: Delving into the implementation and utilization of object locators in DSDL can be beneficial. Object locators play a crucial role in separating structured data set descriptions from unstructured media content, enabling efficient data manipulation and distribution.

By focusing on these areas, the development and refinement of DSDL can be extended to enhance its usability, flexibility, and efficiency in describing diverse AI data sets .

Tables
8
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