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

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2018, Pages 1091-1100

  Li, Yuhong ^a,b   Zhang, Xiaofan ^a   Chen, Deming ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DEEP LEARNING; NEURAL NETWORKS;

CONVOLUTIONAL NEURAL NETWORK; LEARNING METHODS; MEAN ABSOLUTE ERROR; RECEPTION FIELDS; SCENE RECOGNITION; STATE-OF-THE-ART APPROACH; STATE-OF-THE-ART METHODS; STATE-OF-THE-ART PERFORMANCE;

CONVOLUTION;

EID: 85049424909     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2018.00120     Document Type: Conference Paper

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