Interactive Interface For Semantic Segmentation Dataset Synthesis

TL;DR

SynthLab is a modular, user-friendly platform that simplifies the creation of high-quality annotated datasets for semantic segmentation, reducing resource requirements and privacy concerns.

Contribution

The paper introduces SynthLab, a flexible, scalable platform with an interactive interface for efficient dataset synthesis in computer vision tasks.

Findings

User studies show high accessibility for non-experts.

SynthLab enables quick customization of data pipelines.

The platform supports seamless integration of new features.

Abstract

The rapid advancement of AI and computer vision has significantly increased the demand for high-quality annotated datasets, particularly for semantic segmentation. However, creating such datasets is resource-intensive, requiring substantial time, labor, and financial investment, and often raises privacy concerns due to the use of real-world data. To mitigate these challenges, we present SynthLab, consisting of a modular platform for visual data synthesis and a user-friendly interface. The modular architecture of SynthLab enables easy maintenance, scalability with centralized updates, and seamless integration of new features. Each module handles distinct aspects of computer vision tasks, enhancing flexibility and adaptability. Meanwhile, its interactive, user-friendly interface allows users to quickly customize their data pipelines through drag-and-drop actions. Extensive user studies involving a diverse range of users across different ages, professions, and expertise levels, have demonstrated flexible usage, and high accessibility of SynthLab, enabling users without deep technical expertise to harness AI for real-world applications.