面向零样本图像分类的交互式类属性构建方法  

Interactive Class Attribute Construction Method for Zero-Shot Image Classification

作　　者：刘真[1] 徐景胜 颜菁 徐润森 吴向阳[1] Liu Zhen;Xu Jingsheng;Yan Jing;Xu Runsen;Wu Xiangyang(School of Computer Science,Hangzhou Dianzi University,Hangzhou 310018)

机构地区：[1]杭州电子科技大学计算机学院,杭州310018

出　　处：《计算机辅助设计与图形学学报》2025年第2期243-253,共11页Journal of Computer-Aided Design & Computer Graphics

基　　金：浙江省自然科学基金(LY22F020023);浙江省自然科学基金重点项目(LZ22F020015);国家自然科学基金面上项目(61972122)。

摘　　要：零样本图像分类解决了训练和测试数据类别不相交的问题,人类标注属性是一种常用的实现零样本图像分类的辅助知识.为协助专家设计类属性矩阵,提出了一种交互式构建方法,简化了烦琐且缺乏指导的流程.首先,通过一种基于概念的深度学习可解释性方法,在训练集图像数据中提取出可理解的属性信息;然后,采用多视图协作的交互方式,探索和分析已提取属性的重要性.系统提供了全局和局部2种方式,辅助用户设计测试集数据类别的属性值;最后,通过在数据集Animals with Attributes2上进行的案例分析,以及采用李克特量表的用户评估实验,验证了设计方法的有效性和实用性,可以帮助专家用户高效且便捷地完成类属性构建工作.Zero-shot image classification addresses the problem of non-overlapping categories between training and testing data.Human-annotated attributes are a commonly used form of auxiliary knowledge in zero-shot image classification.To assist experts in designing class attribute matrices,an interactive construction method is proposed,simplifying the tedious and unguided process.First,understandable attribute information is extracted from the training dataset images using a concept-based deep learning explainability method.Then,an interactive approach involving multi-view collaboration is employed to explore and analyze the importance of the extracted attributes.The system offers both global and local options to aid users in designing attribute values for testing dataset categories.Finally,the effectiveness and practicality of the design method are validated through case studies on the Animals with Attributes2 dataset and user evaluation experiments using the Likert scale,facilitating efficient and convenient class attribute construction for expert users.

关 键 词：零样本学习 零样本图像分类 可视分析 可解释人工智能 人机协作 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]