mlsanityData to 14 Oct 2026

Learning Efficient Representations for Image-Based Patent Retrieval

arXiv 2308.13749 ↗First posted 26 Aug 2023cs.CVcited by 4

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Abstract (arXiv)

Patent retrieval has been attracting tremendous interest from researchers in intellectual property and information retrieval communities in the past decades. However, most existing approaches rely on textual and metadata information of the patent, and content-based image-based patent retrieval is rarely investigated. Based on traits of patent drawing images, we present a simple and lightweight model for this task. Without bells and whistles, this approach significantly outperforms other counterparts on a large-scale benchmark and noticeably improves the state-of-the-art by 33.5% with the mean average precision (mAP) score. Further experiments reveal that this model can be elaborately scaled up to achieve a surprisingly high mAP of 93.5%. Our method ranks first in the ECCV 2022 Patent Diagram Image Retrieval Challenge.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 26 Aug 2023.

Builds on

19 of 40 references.

References · 19

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7 more
  1. 2021More Photos are All You Need: Semi-Supervised Learning for Fine-Grained Sketch Based Image Retrievalcited by 38
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  4. 2023Efficient Token-Guided Image-Text Retrieval with Consistent Multimodal Contrastive Trainingcited by 10
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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024Natural Language Processing in the Patent Domain: A Surveycited by 20
  2. 2024Large Language Model Informed Patent Image Retrievalcited by 1
  3. 2024Region-aware Image-based Human Action Retrieval with Transformerscited by 1
  4. 2025Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrievalcited by 0