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Crowd Counting with Deep Negative Correlation Learning
Open accessAbstract
Deep convolutional networks (ConvNets) have achieved unprecedented performances on many computer vision tasks. However, their adaptations to crowd counting on single images are still in their infancy and suffer from severe over-fitting. Here we propose a new learning strategy to produce generalizable features by way of deep negative correlation learning (NCL). More specifically, we deeply learn a pool of decorrelated regressors with sound generalization capabilities through managing their intrinsic diversities. Our proposed method, named decorrelated ConvNet (D-ConvNet), is end-to-end-trainable and independent of the backbone fully-convolutional network architectures. Extensive experiments on very deep VGGNet as well as our customized network structure indicate the superiority of D-ConvNet when compared with several state-of-the-art methods. Our implementation will be released at https://github.com/shizenglin/Deep-NCL.
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Cited by
Learning From Synthetic Data for Crowd Counting in the Wild
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TransCrowd: weakly-supervised crowd counting with transformers
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Decoupled Two-Stage Crowd Counting and Beyond
Cheng, J., Xiong, H., Cao, Z., & Lu, H. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3055631 · Cited by 103
Metadata
- Authors
- Shi, Z., Zhang, L., Liu, Y., Cao, X., Ye, Y., Cheng, M.-M., & Zheng, G.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 5382-5390
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2798489385
- Cited by
- 283
- References
- 35
- FWCI
- 14.31
- Type
- conference paper
- Language
- en
- Open access
- green
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