FGENet: Fine-Grained Extraction Network for Congested Crowd Counting-Reference-Cited by-同舟云学术

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FGENet: Fine-Grained Extraction Network for Congested Crowd Counting

Published:2024 Issue: Volume: Page:43-56
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:en
Short-container-title:

Author:

Ma Hao-Yuan,Zhang Li,Wei Xiang-Yi

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-53311-2_4

Reference29 articles.

1. Chen, J., et al.: Run, Don’t Walk: chasing higher FLOPS for faster neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12021–12031 (2023)

2. Cheng, Z.Q., Dai, Q., Li, H., Song, J., Wu, X., Hauptmann, A.G.: Rethinking spatial invariance of convolutional networks for object counting. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19638–19648 (2022)

3. Cheng, Z.Q., Li, J.X., Dai, Q., Wu, X., He, J.Y., Hauptmann, A.: Improving the learning of multi-column convolutional neural network for crowd counting (2019)

4. Dai, M., Huang, Z., Gao, J., Shan, H., Zhang, J.: Cross-head supervision for crowd counting with noisy annotations. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1–5. IEEE (2023)

5. Guo, D., Li, K., Zha, Z., Wang, M.: DADNet: dilated-attention-deformable ConvNet for crowd counting. In: Proceedings of the the 27th ACM International Conference (ACM MM) (2019)

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3. Crowd Counting and Individual Localization Using Pseudo Square Label;IEEE Access;2024

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