Simple Primitives With Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-Shot Learning-Reference-Cited by-同舟云学术

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Simple Primitives With Feasibility- and Contextuality-Dependence for Open-World Compositional Zero-Shot Learning

Published:2024-01 Issue:1 Volume:46 Page:543-560
ISSN:0162-8828
Container-title:IEEE Transactions on Pattern Analysis and Machine Intelligence
language:
Short-container-title:IEEE Trans. Pattern Anal. Mach. Intell.

Author:

Liu Zhe¹^ORCID,Li Yun²^ORCID,Yao Lina²^ORCID,Chang Xiaojun³^ORCID,Fang Wei⁴^ORCID,Wu Xiaojun⁴^ORCID,Saddik Abdulmotaleb El⁵^ORCID

Affiliation:

1. School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia

2. CSIRO's Data61, Eveleigh, NSW, Australia

3. Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, NSW, Australia

4. Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi, China

5. Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence Masdar City, Abu Dhabi, UAE

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

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

Link

http://xplorestaging.ieee.org/ielx7/34/10345401/10274865.pdf?arnumber=10274865

Reference73 articles.

1. Attribute-Modulated Generative Meta Learning for Zero-shot Learning

2. Hierarchical classification of data with long-tailed distributions via global and local granulation

3. Task Aligned Generative Meta-learning for Zero-shot Learning

4. Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks

5. From generalized zero-shot learning to long-tail with class descriptors

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Zero-shot stance detection based on multi-expert collaboration;Scientific Reports;2024-08-05

2. DCMSTRD: End-to-end Dense Captioning via Multi-Scale Transformer Decoding;IEEE Transactions on Multimedia;2024

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