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.

基于专利绘图图像的特征，本文提出了一个简单轻量级的模型，用于专利检索，在大规模基准测试上表现显著优于其他方法，均值平均精度（mAP）得分提高了 33.5%，进一步实验证明此模型能够精确扩展以达到惊人的 93.5% 的 mAP，此方法在 ECCV 2022 专利图像检索挑战赛中排名第一。