Due to the swift growth of patent applications each year, information and
multimedia retrieval approaches that facilitate patent exploration and
retrieval are of utmost importance. Different types of visualizations (e.g.,
graphs, technical drawings) and perspectives (e.g., side view, perspective) are
used to visualize details of innovations in patents. The classification of
these images enables a more efficient search and allows for further analysis.
So far, datasets for image type classification miss some important
visualization types for patents. Furthermore, related work does not make use of
recent deep learning approaches including transformers. In this paper, we adopt
state-of-the-art deep learning methods for the classification of visualization
types and perspectives in patent images. We extend the CLEF-IP dataset for
image type classification in patents to ten classes and provide manual ground
truth annotations. In addition, we derive a set of hierarchical classes from a
dataset that provides weakly-labeled data for image perspectives. Experimental
results have demonstrated the feasibility of the proposed approaches. Source
code, models, and dataset will be made publicly available.

本篇论文采用最先进的深度学习方法，对专利图像中的可视化类型和视角进行分类，并对 CLEF-IP 数据集进行了扩展和手动标注，实验结果证明了该方法的可行性。

专利中的可视化类型和视角分类

Classification of Visualization Types and Perspectives in Patents

We present methods for the automatic classification of patent applications
using an annotated dataset provided by the organizers of the ALTA 2018 shared
task - Classifying Patent Applications. The goal of the task is to use
computational methods to categorize patent applications according to a
coarse-grained taxonomy of eight classes based on the International Patent
Classification (IPC). We tested a variety of approaches for this task and the
best results, 0.778 micro-averaged F1-Score, were achieved by SVM ensembles
using a combination of words and characters as features. Our team, BMZ, was
ranked first among 14 teams in the competition.

使用 ALTA 2018 共享任务提供的已注释数据集，我们提供了自动分类专利申请的方法。通过多种方法的测试，利用 SVM 集成使用单词和字符组合作为特征，最佳结果在 14 支队伍中排名第一的 0.778 微平均 F1 - 值实现了将专利申请按照基于国际专利分类（IPC）的粗粒度分类法分为八个类别的目标。