mlsanityData to 7 Oct 2026

DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing Understanding

arXiv 2311.04098 ↗First posted 7 Nov 2023cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 68 references that are in the map. First posted on arXiv 7 Nov 2023.

Builds on

25 of 68 references.

References · 25

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13 more
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Cited by year

Citing papers per year: 2024: 2; 2025: 2.

4 papers.

  1. 2024A Survey on Patent Analysis: From NLP to Multimodal AIcited by 10
  2. 2024Large Language Model Informed Patent Image Retrievalcited by 1
  3. 2025Patent Figure Classification using Large Vision-language Modelscited by 0
  4. 2025Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrievalcited by 0