CounTr: An End-to-End Transformer Approach for Crowd Counting and Density Estimation

Author:

Bai Haoyue,He Hao,Peng Zhuoxuan,Dai Tianyuan,Chan S.-H. Gary

Publisher

Springer Nature Switzerland

Reference46 articles.

1. Aitken, A., Ledig, C., Theis, L., Caballero, J., Wang, Z., Shi, W.: Checkerboard artifact free sub-pixel convolution: A note on sub-pixel convolution, resize convolution and convolution resize. arXiv:1707.02937 (2017)

2. Bai, H., Mao, J., Chan, S.H.G.: A survey on deep learning-based single image crowd counting: Network design, loss function and supervisory signal. Neurocomputing (2022)

3. Bai, H., Wen, S., Gary Chan, S.H.: Crowd counting on images with scale variation and isolated clusters. In: ICCVW (2019)

4. Bao, H., Dong, L., Wei, F.: Beit: Bert pre-training of image transformers. arXiv:2106.08254 (2021)

5. Lecture Notes in Computer Science;X Cao,2018

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