Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting-Reference-Cited by-同舟云学术

Improving the Learning of Multi-column Convolutional Neural Network for Crowd Counting

Published:2019-10-15 Issue: Volume: Page:
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Container-title:Proceedings of the 27th ACM International Conference on Multimedia
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Author:

Cheng Zhi-Qi¹,Li Jun-Xiu²,Dai Qi³,Wu Xiao⁴,He Jun-Yan⁴,Hauptmann Alexander G.⁵

Affiliation:

1. Carnegie Mellon University & Southwest Jiaotong University, Pittsburgh, PA, USA

2. Southwest Jiaotong University & Microsoft Research, Chengdu, China

3. Microsoft Research, Beijing, China

4. Southwest Jiaotong University, Chengdu, China

5. Carnegie Mellon University, Pittsburgh, PA, USA

Funder

Foundation for Department of Transportation of Henan Province

Sichuan Science and Technology Innovation Seedling Fund

Excellent Doctoral Dissertation of Southwest Jiaotong University

U.S. Department of Commerce

National Institute of Standards and Technology and by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DOI/IBC)

China Scholarship Council

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3343031.3350898

Reference70 articles.

1. Jimmy Ba and Rich Caruana. 2014. Do deep nets really need to be deep?. In Advances in neural information processing systems. 2654--2662. Jimmy Ba and Rich Caruana. 2014. Do deep nets really need to be deep?. In Advances in neural information processing systems. 2654--2662.

2. Unsupervised Bayesian Detection of Independent Motion in Crowds

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