Masked Image Self-Learning and Knowledge Distillation for Source-Free Unsupervised Domain Adaptation in Crowd Counting-Reference-Cited by-同舟云学术

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Masked Image Self-Learning and Knowledge Distillation for Source-Free Unsupervised Domain Adaptation in Crowd Counting

Published:2023-11-03 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)
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Author:

Zeng Jia¹,Ma Chaoqun¹,Shao Penghui¹,Qing Anyong¹,Wang Yang²

Affiliation:

1. Southwest Jiaotong University,School of Electrical Engineering,Chengdu,China

2. Concordia University,Department of Computer Science and Software Engineering,Montreal,Canada

Funder

Research and Development

China Scholarship Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10424714/10424727/10424929.pdf?arnumber=10424929

Reference20 articles.

1. Crowd Scene Understanding from Video

2. Counting People Crossing a Line Using Integer Programming and Local Features

3. Measuring Crowd Collectiveness

4. Bi-level Alignment for Cross-Domain Crowd Counting

5. Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation

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1. MTSC-Net: A Semi-Supervised Counting Network for Estimating the Number of Slash pine New Shoots;Plant Phenomics;2024-01

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