Relation-based Discriminative Cooperation Network for Zero-Shot Classification
Relation-based Discriminative Cooperation Network for Zero-Shot Classification
复制标题
基于关系的零样本分类判别合作网络
DOI：
10.1016/j.patcog.2021.108024
复制
发表时间：
2021
影响因子：
8
通讯作者：
Ling Shao
中科院分区：
文献类型：
--
作者：
Yang Liu;Xinbo Gao;Quanxue Gao;Jungong Han;Ling Shao
零镜头学习(ZSL)的目的是根据学习到的视觉特征和语义特征之间的关系，将相关语义对应的类别指定为未见样本的标签。然而，大多数典型的ZSL模型都面临领域偏差问题，这导致未见或测试样本容易被错误地分类为可见或训练类别。针对这一问题，本文提出了一种基于关系的ZSL鉴别协作网络模型。该模型通过一组关系，有效地利用了协同语义所跨越的稳健度量空间。另一方面，我们设计了关系网络来度量视觉特征和嵌入语义之间的关系，验证信息将指导嵌入模块学习更多的区分性信息。最后，在六个基准上对所提出的RDCN模型进行了验证，并通过大量的实验验证了该方法在传统零镜头设置和更真实的广义零镜头设置上的优越性。
Zero-shot learning (ZSL) aims to assign the category corresponding to the relevant semantic as the label of the unseen sample based on the relationship between the learned visual and semantic features. However, most typical ZSL models faced with the domain bias problem, which leads to unseen or test samples being easily misclassified into seen or training categories. To handle this problem, we propose a relation-based discriminative cooperation network (RDCN) model for ZSL in this work. The proposed model effectively utilize the robust metric space spanned by the cooperated semantics with the help of a set of relations. On the other hand, we devise the relation network to measure the relationship between the visual features and embedded semantics, and the validation information will guide the embedding module to learn more discriminative information. At last, the proposed RDCN model is validated on six benchmarks, and extensive experiments demonstrate the superiority of proposed method over most existing ZSL models on the traditional zero-shot setting and the more realistic generalized zero-shot setting.