Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting-Reference-Cited by-同舟云学术

Represent, Compare, and Learn: A Similarity-Aware Framework for Class-Agnostic Counting

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Shi Min¹,Lu Hao¹,Feng Chen¹,Liu Chengxin¹,Cao Zhiguo¹

Affiliation:

1. Ministry of Education School of Artificial Intelligence and Automation, Huazhong University of Science and Technology,Key Laboratory of Image Processing and Intelligent Control,China

Funder

National Natural Science Foundation of China

Chinese Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09879984.pdf?arnumber=9879984

Reference44 articles.

1. From Open Set to Closed Set: Counting Objects by Spatial Divide-and-Conquer

2. Distance metric learning for large margin nearest neighbor clas-sification;weinberger;Proc Adv Neural Inf Process Syst,0

3. Circle Loss: A Unified Perspective of Pair Similarity Optimization

4. To Choose or to Fuse? Scale Selection for Crowd Counting

5. Rethinking Counting and Localization in Crowds: A Purely Point-Based Framework

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