Interactive Interface For Semantic Segmentation Dataset Synthesis
This addresses the problem of dataset creation for semantic segmentation, which is costly and privacy-sensitive, by providing a user-friendly tool, though it appears incremental as it builds on existing synthesis methods with a focus on interface improvements.
The paper tackles the resource-intensive and privacy-concerning process of creating annotated datasets for semantic segmentation by presenting SynthLab, a modular platform with an interactive interface that enables users, including those without technical expertise, to customize data pipelines efficiently, as demonstrated through extensive user studies showing high accessibility and flexible usage.
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.