Explicit Metric-Based Multiconcept Multi-Instance Learning With Triplet and Superbag-Reference-Cited by-同舟云学术

Explicit Metric-Based Multiconcept Multi-Instance Learning With Triplet and Superbag

Published:2021 Issue: Volume: Page:1-10
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Chi Ziqiu¹,Wang Zhe²^ORCID,Du Wenli³

Affiliation:

1. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China, and also with the Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China.

2. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China, and also with the Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai 200237, China (e-mail: wangzhe@ecust.edu.cn)

3. Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China.

Funder

Shanghai Science and Technology Program Distributed and generative few-shot algorithm and theory research

Natural Science Foundation of China

National Science Foundation of China for Distinguished Young Scholars

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/09410432.pdf?arnumber=9410432

Cited by 7 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. Multi-scale multi-instance contrastive learning for whole slide image classification;Engineering Applications of Artificial Intelligence;2024-12

2. VPE-WSVAD: Visual prompt exemplars for weakly-supervised video anomaly detection;Knowledge-Based Systems;2024-09

3. Multi-Instance Learning with One Side Label Noise;ACM Transactions on Knowledge Discovery from Data;2024-03-26

4. Single-Stage Broad Multi-Instance Multi-Label Learning (BMIML) With Diverse Inter-Correlations and Its Application to Medical Image Classification;IEEE Transactions on Emerging Topics in Computational Intelligence;2024-02

5. A fisher score-based multi-instance learning method assisted by mixture of factor analysis;Neurocomputing;2022-10