Bi-level Alignment for Cross-Domain Crowd Counting-Reference-Cited by-同舟云学术

Bi-level Alignment for Cross-Domain Crowd Counting

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

Gong Shenjian¹,Zhang Shanshan¹,Yang Jian¹,Dai Dengxin²,Schiele Bernt²

Affiliation:

1. School of Computer Science and Engineering, Nanjing University of Science and Technology,PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, and Jiangsu Key Lab of Image and Video Understanding for Social Security

2. MPI Informatics

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09878759.pdf?arnumber=9878759

Reference32 articles.

1. Unpaired image-to-image translation using cycleconsistent adversarial networks;zhu;ICCV,2017

2. Single-Image Crowd Counting via Multi-Column Convolutional Neural Network

3. Data-Driven Crowd Understanding: A Baseline for a Large-Scale Crowd Dataset

4. Nascount: Counting-by-density with neural architecture search;hu;ECCV,2020

5. FSDR: Frequency Space Domain Randomization for Domain Generalization

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