Abstract
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-ofthe-art methods. Our implementation will be released at
https://github.com/shizenglin/Deep-NCL