用草图+大模型降低图像标注门槛，提升可解释性
It's Not Just Labeling -- A Research on LLM Generated Feedback Interpretability and Image Labeling Sketch Features
- 用草图输入配合大模型生成反馈，降低非专家使用门槛
- 发现草图特征与大模型反馈质量存在关联，可优化标注可靠性
- 适合希望提升标注可解释性与普适性的研究者和开发者
训练数据质量对交通、医疗、机器人等领域的机器学习应用至关重要。然而，准确的图像标注通常依赖耗时且需专家参与的方法，反馈有限。本研究提出一种基于草图的标注方法，由大语言模型（LLMs）支持，以降低技术门槛并提升可访问性。利用合成数据集，探究草图识别特征与LLM反馈指标的关系，旨在提升LLM辅助标注的可靠性与可解释性。同时研究提示策略和草图变化对反馈质量的影响。主要贡献是构建了一个基于草图的虚拟助手，简化非专家的标注流程，并推动了大模型驱动标注工具在可扩展性、可访问性和可解释性方面的进步。
原文摘要 · Abstract (English)
The quality of training data is critical to the performance of machine learning applications in domains like transportation, healthcare, and robotics. Accurate image labeling, however, often relies on time-consuming, expert-driven methods with limited feedback. This research introduces a sketch-based annotation approach supported by large language models (LLMs) to reduce technical barriers and enhance accessibility. Using a synthetic dataset, we examine how sketch recognition features relate to LLM feedback metrics, aiming to improve the reliability and interpretability of LLM-assisted labeling. We also explore how prompting strategies and sketch variations influence feedback quality. Our main contribution is a sketch-based virtual assistant that simplifies annotation for non-experts and advances LLM-driven labeling tools in terms of scalability, accessibility, and explainability.
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