Learning Efficient Representations for Image-Based Patent Retrieval
Hongsong Wang Yuqi Zhang
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.