Explanatory Object Part Aggregation for Zero-Shot Learning

Author:

Chen Xin1ORCID,Deng Xiaoling1ORCID,Lan Yubin1ORCID,Long Yongbing1ORCID,Weng Jian2ORCID,Liu Zhiquan2ORCID,Tian Qi3ORCID

Affiliation:

1. College of Electronic Engineering, College of Artificial Intelligence, National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying Technology, Guangdong Laboratory for Lingnan Modern Agriculture, Guangdong Engineering Technology Research Center of Smart Agriculture, South China Agricultural University, Guangzhou, China

2. College of Cyber Security, Jinan University, Guangzhou, China

3. Cloud BU, Huawei Technologies, Shenzhen, China

Funder

National Natural Science Foundation of China

Basic and Applied Basic Research Foundation of Guangdong Province

Guangzhou Basic and Applied Basic Research Foundation

Key-Area Research and Development Program of Guangdong Province

China Agriculture Research System

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Reference71 articles.

1. ImageNet classification with deep convolutional neural networks

2. Learning to detect unseen object classes by between-class attribute transfer

3. Zero-shot learning with semantic output codes;Palatucci

4. Describing objects by their attributes

5. Distributed representations of words and phrases and their compositionality;Mikolov

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