.png){kind=icon}
返回
Multi-modal generative adversarial network for zero-shot learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2020.105847.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a novel approach for Zero-Shot Learning (ZSL), where the test instances are from the novel categories that no visual data are available during training. The existing approaches typically address ZSL by embedding the visual features into a category-shared semantic space. However, these embedding-based approaches easily suffer from the heterogeneity gap issue since a single type of class semantic prototype cannot characterize the categories well. To alleviate this issue, we assume that different class semantics reflect different views of the corresponding class, and thus fuse various types of class semantic prototypes resided in different semantic spaces with a feature fusion network to generate pseudo visual features. Through the adversarial mechanism of the real visual features and the fused pseudo visual features, the complementary semantics in various spaces are effectively captured. Experimental results on three benchmark datasets demonstrate that the proposed approach achieves impressive performances on both traditional ZSL and generalized ZSL tasks. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Zero-shot learning
Multi-modal generative adversarial network
Feature fusion
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
机构
引用论文
Earthquakes and related catastrophic events, Island of Hawaii, November 29, 1975: A preliminary report.png)
Circular
OAAI
CircularIF0
OAAI分享收藏
查看更多内容