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Patent retrieval has been attracting tremendous interest from researchers inintellectual property and information retrieval communities in the pastdecades. However, most existing approaches rely on textual and metadatainformation of the patent, and content-based image-based patent retrieval israrely investigated. Based on traits of patent drawing images, we present asimple and lightweight model for this task. Without bells and whistles, thisapproach significantly outperforms other counterparts on a large-scalebenchmark and noticeably improves the state-of-the-art by 33.5% with the meanaverage precision (mAP) score. Further experiments reveal that this model canbe elaborately scaled up to achieve a surprisingly high mAP of 93.5%. Ourmethod ranks first in the ECCV 2022 Patent Diagram Image Retrieval Challenge.

Learning Efficient Representations for Image-Based Patent Retrieval