Recent advances in computer vision (CV) and natural language processing have
been driven by exploiting big data on practical applications. However, these
research fields are still limited by the sheer volume, versatility, and
diversity of the available datasets. CV tasks, such as image captioning, which
has primarily been carried out on natural images, still struggle to produce
accurate and meaningful captions on sketched images often included in
scientific and technical documents. The advancement of other tasks such as 3D
reconstruction from 2D images requires larger datasets with multiple
viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more
than 2.7 million technical drawings with 132,890 object names and 22,394
viewpoints extracted from 14 years of US design patent documents. We
demonstrate the usefulness of DeepPatent2 with conceptual captioning. We
further provide the potential usefulness of our dataset to facilitate other
research areas such as 3D image reconstruction and image retrieval.

最近计算机视觉（CV）和自然语言处理的快速发展受益于对实际应用中的大数据的利用。然而，这些研究领域仍受到可用数据集的数量、多样性和多样性的限制。本研究介绍了 DeepPatent2，这是一个大规模数据集，提供了超过 270 万张技术绘图，其中包含 132,890 个对象名称和 22,394 个视角，提取自 14 年的美国设计专利文件。我们展示了 DeepPatent2 在概念字幕生成方面的实用性，同时还提供了我们的数据集在促进其他研究领域如三维图像重建和图像检索方面的潜在用途。