An Unsupervised Trademark Retrieval Method Based on Attention Mechanism

Liang Guan-shu; Cao Jiang-zhong; Dai Qing-yun; Huang Yun-fei

doi:10.12052/gdutxb.200027

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An Unsupervised Trademark Retrieval Method Based on Attention Mechanism

更新时间：2026-05-27

- An Unsupervised Trademark Retrieval Method Based on Attention Mechanism
- Vol. 37, Issue 6, Pages: 41-49(2020)
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 广东技术师范大学广东省知识产权大数据重点实验室,广东,广州,510665
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.200027
  CLC： TP391.4
- Online First：02 November 2020，
  
  Published：2020
- 稿件说明：
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梁观术, 曹江中, 戴青云, 黄云飞. 一种基于注意力机制的无监督商标检索方法[J]. 广东工业大学学报, 2020, 37(6): 41-49.

Liang Guan-shu, Cao Jiang-zhong, Dai Qing-yun, et al. An Unsupervised Trademark Retrieval Method Based on Attention Mechanism[J]. 2020, 37(6): 41-49.
梁观术, 曹江中, 戴青云, 黄云飞. 一种基于注意力机制的无监督商标检索方法[J]. 广东工业大学学报, 2020, 37(6): 41-49. DOI： 10.12052/gdutxb.200027.

Liang Guan-shu, Cao Jiang-zhong, Dai Qing-yun, et al. An Unsupervised Trademark Retrieval Method Based on Attention Mechanism[J]. 2020, 37(6): 41-49. DOI： 10.12052/gdutxb.200027.

摘要

针对现有商标特征提取方法无法有效捕捉重点区域的关键信息和图像标注成本过高的问题

提出一种基于注意力机制的无监督商标检索方法。该方法基于实例区分算法

将注意力模块同时运用在神经网络特征映射层的空间维度和通道维度上

通过对各个通道进行权重的分配以及对空间变换参数的学习

增强无监督网络的特征表达能力。该方法在公开的商标数据集上进行了验证

实验表明检索效果优于传统的商标检索方法

甚至优于一些有监督商标检索方法。

Abstract

In order to solve the deficiency in capturing key information in key areas and the high cost of image annotation in the existing trademark retrieval methods

this paper proposes an unsupervised trademark retrieval method based on attention mechanism. The method applies the attention module to both the spatial dimension and the channel dimension of the feature map layer in the neural network of instance discrimination. Through assigning weights to each channel and learning the spatial transformation parameters

the unsupervised network improve its ability of extracting feature. We further validate the effectiveness of our method on the public trademark datasets and the experiments demonstrate that the proposed method in the paper is better than the traditional trademark retrieval methods

and even surpasses some supervised trademark retrieval methods.

关键词

Keywords

references

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