SynthLab通过交互式界面实现高效合成语义分割数据集
Interactive Interface For Semantic Segmentation Dataset Synthesis
- 模块化架构支持灵活定制数据生成流程
- 用户可拖拽操作快速构建数据管道
- 适合无技术背景者用于真实场景数据合成
人工智能与计算机视觉的快速发展显著提升了对高质量标注数据集的需求，尤其在语义分割领域。然而，构建此类数据集成本高昂，需大量时间、人力与资金投入，且使用真实数据常引发隐私问题。为应对这些挑战，我们提出SynthLab，一个包含模块化视觉数据合成平台与友好交互界面的系统。其模块化设计支持便捷维护、集中更新与新功能无缝集成，各模块分别处理计算机视觉任务的不同方面，提升灵活性与适应性。交互式界面支持用户通过拖拽操作快速定制数据处理流程。多项用户研究涵盖不同年龄、职业与技术水平的参与者，验证了SynthLab的高可用性与广泛适用性，使非技术背景用户也能利用AI完成真实场景的数据合成。
原文摘要 · Abstract (English)
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.
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