Interactive Interface For Semantic Segmentation Dataset Synthesis

[2506.23470] Interactive Interface For Semantic Segmentation Dataset Synthesis Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2506.23470 (cs) [Submitted on 30 Jun 2025 ( v1 ), last revised 11 Apr 2026 (this version, v2)] Title: Interactive Interface For Semantic Segmentation Dataset Synthesis Authors: Ngoc-Do Tran , Minh-Tuan Huynh , Tam V. Nguyen , Minh-Triet Tran , Trung-Nghia Le View a PDF of the paper titled Interactive Interface For Semantic Segmentation Dataset Synthesis, by Ngoc-Do Tran and Minh-Tuan Huynh and Tam V. Nguyen and Minh-Triet Tran and Trung-Nghia Le View PDF HTML (experimental) 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. Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2506.23470 [cs.CV] (or arXiv:2506.23470v2 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2506.23470 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Trung Nghia Le [ view email ] [v1] Mon, 30 Jun 2025 02:23:34 UTC (9,703 KB) [v2] Sat, 11 Apr 2026 18:32:38 UTC (11,841 KB) Full-text links: Access Paper: View a PDF of the paper titled Interactive Interface For Semantic Segmentation Dataset Synthesis, by Ngoc-Do Tran and Minh-Tuan Huynh and Tam V. Nguyen and Minh-Triet Tran and Trung-Nghia Le View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2025-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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