Complementary Parts Contrastive Learning for Fine-Grained Weakly Supervised Object Co-Localization

Author:

Ma Lei1ORCID,Zhao Fan1,Hong Hanyu1ORCID,Wang Lei1ORCID,Zhu Ying1ORCID

Affiliation:

1. School of Electrical and Information Engineering, Hubei Key Laboratory of Optical Information and Pattern Recognition, Wuhan Institute of Technology, Wuhan, P. R. China

Funder

National Natural Science Foundation of China

Startup Foundation of the Wuhan Institute of Technology

Guiding Scientific Research Project of Hubei Provincial Department of Education

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Reference75 articles.

1. Rethinking the Route Towards Weakly Supervised Object Localization

2. Very deep convolutional networks for large-scale image recognition;simonyan;Proc Int Conf Learn Represent,2015

3. Online Refinement of Low-level Feature Based Activation Map for Weakly Supervised Object Localization

4. EfficientNet: Rethinking model scaling for convolutional neural networks;tan;Mach Learn Res,2019

5. C2AM: Contrastive learning of class-agnostic activation map for weakly supervised object localization and semantic segmentation;xie;Proc IEEE/CVF Conf Comput Vis Pattern Recognit (CVPR),2022

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