面向大规模多模态数据集的开放词汇工业缺陷理解
Towards Open-Vocabulary Industrial Defect Understanding with a Large-Scale Multimodal Dataset
AI总结：
本文提出IMDD-1M大规模多模态数据集，训练了适用于工业场景的视觉-语言基础模型，通过高效微调实现领域适应，提升工业检测与生成性能。
摘要：
我们介绍了IMDD-1M，这是首个大规模工业多模态缺陷数据集，包含100万对对齐的图像-文本对，旨在推进制造业和质量检测中的多模态学习。IMDD-1M包含覆盖60多种材料类别和400多种缺陷类型的高分辨率真实世界缺陷，每种缺陷都配有专家验证的注释和详细的细粒度文本描述，描述缺陷位置、严重程度和上下文属性。该数据集能够支持广泛的应用，包括分类、分割、检索、描述生成和生成模型。基于IMDD-1M，我们训练了一个基于扩散的视觉-语言基础模型，专门针对工业场景进行训练。该模型是一种具有通用性的基础模型，可以通过轻量级微调高效适应到专门领域。在少于专精专家模型所需5%的任务特定数据的情况下，它实现了可比的性能，突显了数据高效基础模型适应在工业检测和生成中的潜力，为可扩展、领域适应和知识驱动的制造智能铺平了道路。更多细节和资源可在该URL中找到：https://ninaneon.github.io/projectpage/
英文摘要：
We present IMDD-1M, the first large-scale Industrial Multimodal Defect Dataset comprising 1,000,000 aligned image-text pairs, designed to advance multimodal learning for manufacturing and quality inspection. IMDD-1M contains high-resolution real-world defects spanning over 60 material categories and more than 400 defect types, each accompanied by expert-verified annotations and fine-grained textual descriptions detailing defect location, severity, and contextual attributes. This dataset enables a wide spectrum of applications, including classification, segmentation, retrieval, captioning, and generative modeling. Building upon IMDD-1M, we train a diffusion-based vision-language foundation model from scratch, specifically tailored for industrial scenarios. The model serves as a generalizable foundation that can be efficiently adapted to specialized domains through lightweight fine-tuning. With less than 5% of the task-specific data required by dedicated expert models, it achieves comparable performance, highlighting the potential of data-efficient foundation model adaptation for industrial inspection and generation, paving the way for scalable, domain-adaptive, and knowledge-grounded manufacturing intelligence. Additional details and resources can be found in this URL: https://ninaneon.github.io/projectpage/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
