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arxiv: 2506.23470 · v2 · submitted 2025-06-30 · 💻 cs.CV

Interactive Interface For Semantic Segmentation Dataset Synthesis

Ngoc-Do Tran , Minh-Tuan Huynh , Tam V. Nguyen , Minh-Triet Tran , Trung-Nghia Le This is my paper

Pith reviewed 2026-05-19 08:13 UTC · model grok-4.3

classification 💻 cs.CV
keywords semantic segmentationdataset synthesissynthetic datainteractive interfacemodular platformdrag-and-dropuser accessibilitycomputer vision
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The pith

SynthLab uses a modular platform and drag-and-drop interface to let non-experts create custom semantic segmentation datasets.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents SynthLab to address the high costs, labor demands, and privacy issues involved in building annotated datasets for semantic segmentation. Its modular structure divides computer vision tasks into separate components that support maintenance and the addition of new capabilities through central updates. The accompanying interface lets users adjust data generation steps with simple drag-and-drop operations instead of writing code. User studies with participants of varying ages, professions, and skill levels indicate that the system is usable by people who lack deep technical training, opening AI tools to broader real-world use.

Core claim

SynthLab consists of a modular platform for visual data synthesis and a user-friendly interface in which each module addresses a distinct computer vision task, enabling users to customize data pipelines through drag-and-drop actions and thereby produce semantic segmentation datasets while avoiding the resource and privacy burdens of real-world collection.

What carries the argument

The modular architecture paired with the drag-and-drop interactive interface, which isolates different aspects of data synthesis into separate modules for flexibility and permits rapid pipeline customization without coding.

If this is right

  • Non-technical users can generate and apply their own semantic segmentation datasets for practical computer vision work.
  • Centralized updates allow the platform to scale and incorporate new synthesis features without disrupting existing users.
  • Reliance on synthesized rather than real data reduces privacy risks during dataset creation.
  • The separation of tasks into modules supports adaptation to other computer vision annotation needs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Wider use of such interfaces could shorten the time between identifying a new segmentation problem and obtaining training data for it.
  • The modular separation might simplify combining synthetic data with limited real data in hybrid training setups.
  • If the generated datasets prove reliable, the approach could lower barriers for deploying segmentation models in fields with strict data-sharing rules.

Load-bearing premise

That the datasets generated by non-expert users through drag-and-drop customizations are sufficiently accurate and complete to train effective semantic segmentation models.

What would settle it

A controlled test in which non-expert users build datasets via the interface, a model is trained on those datasets, and the model's segmentation accuracy is measured against the same model trained on expert-annotated real-world data using standard benchmarks.

Figures

Figures reproduced from arXiv: 2506.23470 by Minh-Triet Tran, Minh-Tuan Huynh, Ngoc-Do Tran, Tam V. Nguyen, Trung-Nghia Le.

Figure 1
Figure 1. Figure 1: Through an interactive interface, SynthLab enables end-users to create their data generation pipeline quickly via [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: SynthLab pipeline can execute single computer vision tasks like classification, segmentation, and image generation. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Fundamental design of SynthLab. for improved outcomes, making it particularly suitable for users accustomed to visual workflow systems. 3 Proposed SynthLab Platform 3.1 Fundamental Design SynthLab is the back-side platform with a simple yet effective de￾sign. The platform acts as a core logic and provides artifacts for the interface. Furthermore, SynthLab is also designed to be used from the end-client-sid… view at source ↗
Figure 4
Figure 4. Figure 4: SynthLab’s interface includes three main parts, the modules pool area (1), the settings area (2), and the playground (3). [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Pipeline customization is flexible in SynthLab. [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Designed workflow for semantic segmentation dataset synthesis, including three big components: prompting module [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Designed workflow to label uncommon objects. [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Examples of uncommon objects [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example of chemistry-related pipeline. with computer vision and machine learning expertise. Participants explored SynthLab and rated it on a 1–5 scale (1: least satisfied, 5: most satisfied) across three criteria: user-friendly interface, ease of usage, and application concept. SynthLab earned a strong overall mean opinion score (MOS) of 4.2 ( [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: This pipeline was created by an undergraduate student in Task 2. The pipeline, involving GroundedSAM [ [PITH_FULL_IMAGE:figures/full_fig_p008_11.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces SynthLab, a modular platform for visual data synthesis combined with an interactive drag-and-drop interface for generating semantic segmentation datasets. It claims that the architecture supports easy maintenance, scalability, and feature integration, while the interface enables non-expert users to customize pipelines; these claims are supported by assertions of extensive user studies with diverse participants demonstrating high accessibility and flexible usage for real-world AI applications.

Significance. If the user-study results and dataset usability claims hold under scrutiny, the work could meaningfully reduce barriers to creating annotated semantic segmentation data, mitigating costs, labor, and privacy issues associated with real-world collection. This has potential practical impact in computer vision applications where custom datasets are needed but technical expertise is limited.

major comments (2)
  1. [Abstract] Abstract: The central claim that 'extensive user studies involving a diverse range of users across different ages, professions, and expertise levels have demonstrated flexible usage and high accessibility' is load-bearing for the accessibility conclusion, yet the manuscript provides no details on study design, participant count, recruitment method, specific tasks (e.g., drag-and-drop pipelines), metrics collected, or statistical analysis. Without this information the conclusion cannot be evaluated.
  2. [Modular platform description] Modular platform description: The statement that the modular architecture 'enables easy maintenance, scalability with centralized updates, and seamless integration of new features' and that 'drag-and-drop actions' produce usable semantic segmentation datasets lacks any concrete mechanism, validation procedure, or example showing how pixel-accurate labels are generated or ensured. This directly affects the weakest assumption that the interface yields production-ready data.
minor comments (2)
  1. [Abstract] The abstract and system overview would benefit from explicit comparison to existing synthetic data tools or annotation interfaces to better situate the novelty.
  2. [System overview] Notation for modules and pipeline components is introduced at a high level; a diagram or table listing module responsibilities and data flow would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each of the major comments below and indicate the revisions we plan to make in the updated version.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that 'extensive user studies involving a diverse range of users across different ages, professions, and expertise levels have demonstrated flexible usage and high accessibility' is load-bearing for the accessibility conclusion, yet the manuscript provides no details on study design, participant count, recruitment method, specific tasks (e.g., drag-and-drop pipelines), metrics collected, or statistical analysis. Without this information the conclusion cannot be evaluated.

    Authors: We agree that the abstract and manuscript would benefit from more detailed information on the user studies to allow proper evaluation of the accessibility claims. In the revised manuscript, we have expanded the relevant sections to include the study design, number of participants, recruitment methods, specific tasks performed using the drag-and-drop interface, collected metrics such as usability scores and task success rates, and the statistical analysis performed. These additions provide the necessary context for the claims made. revision: yes

  2. Referee: [Modular platform description] Modular platform description: The statement that the modular architecture 'enables easy maintenance, scalability with centralized updates, and seamless integration of new features' and that 'drag-and-drop actions' produce usable semantic segmentation datasets lacks any concrete mechanism, validation procedure, or example showing how pixel-accurate labels are generated or ensured. This directly affects the weakest assumption that the interface yields production-ready data.

    Authors: We thank the referee for this important point. The current description is indeed high-level and does not sufficiently detail the label generation process. We have revised the manuscript to include a concrete description of the mechanism: the drag-and-drop actions set parameters for the synthesis modules, which generate both the visual data and the corresponding semantic labels using a combination of procedural modeling and rendering techniques that ensure pixel accuracy by construction. We have also added a validation procedure involving comparison with manually annotated subsets and included an example pipeline in the revised text. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive system paper with no derivation chain

full rationale

This is a system-description paper presenting SynthLab as a modular platform and drag-and-drop interface for semantic segmentation dataset synthesis. The abstract and provided text contain no equations, no fitted parameters, no predictions derived from first principles, and no load-bearing self-citations that reduce claims to their own inputs. The user-study assertion is an empirical claim whose evidentiary strength can be questioned separately, but it does not create a self-definitional or fitted-input loop. The paper is therefore self-contained against external benchmarks and receives the default non-circularity finding for descriptive work.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The paper introduces SynthLab as a new system whose value depends on unstated assumptions about the quality of generated labels and the representativeness of the user-study population; no free parameters or invented physical entities are defined.

axioms (2)
  • domain assumption Modular architecture enables easy maintenance, scalability, and seamless integration of new features
    Stated in the abstract as a property of the platform design without further justification or measurement.
  • domain assumption Drag-and-drop interface allows users without deep technical expertise to customize data pipelines
    Central usability claim that is asserted rather than derived from first principles.
invented entities (1)
  • SynthLab modular platform no independent evidence
    purpose: Provide visual data synthesis for semantic segmentation
    New named system introduced to address dataset creation challenges; no independent falsifiable prediction is supplied beyond the platform itself.

pith-pipeline@v0.9.0 · 5699 in / 1439 out tokens · 28130 ms · 2026-05-19T08:13:55.965481+00:00 · methodology

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Reference graph

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