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Leveraging Self-Supervision 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)
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Author:

Liu Weizhe1,Durasov Nikita2,Fua Pascal2

Affiliation:

1. Tencent AI Lab

2. CVLab, EPFL

Publisher

IEEE

Reference115 articles.

1. CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

2. Enhanced Trans-port Distance for Unsupervised Domain Adaptation;li;Conference on Computer Vision and Pattern Recognition,2020

3. Attending to Discriminative Certainty for Domain Adaptation

4. Adam: A Method for Stochas-tic Optimisation;kingma;International Conference on Learning Representations,2015

5. Unsu-pervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach;kim;Conference on Computer Vision and Pattern Recognition,2019

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1. Crowd Counting Using Meta-Test-Time Adaptation;International Journal of Neural Systems;2024-09-09

2. MetaUSACC: Unlabeled scene adaptation for crowd counting via meta-auxiliary learning;Expert Systems with Applications;2024-08

3. HSNet: Crowd counting via hierarchical scale calibration and spatial attention;Engineering Applications of Artificial Intelligence;2024-07

4. MSFFT: Multi-Scale Feature Fusion Transformer for cross platform vehicle re-identification;Neurocomputing;2024-05

5. Unlabeled scene adaptive crowd counting via meta-ensemble learning;Transportation Research Part C: Emerging Technologies;2024-02
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