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Deep Sampling Networks
Bolun Cai, Xiangmin Xu, Kailing Guo +2
Deep convolutional neural networks achieve excellent image up-sampling performance. However, CNN-based methods tend to restore high-resolution results highly depending on tradition…
FReLU: Flexible Rectified Linear Units for Improving Convolutional Neural Networks
Suo Qiu, Xiangmin Xu, Bolun Cai
Rectified linear unit (ReLU) is a widely used activation function for deep convolutional neural networks. However, because of the zero-hard rectification, ReLU networks miss the be…
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
Xiaoyi Jia, Xiangmin Xu, Bolun Cai +1
Methods based on convolutional neural network (CNN) have demonstrated tremendous improvements on single image super-resolution. However, the previous methods mainly restore images…
Multi-scale Convolutional Neural Networks for Crowd Counting
Lingke Zeng, Xiangmin Xu, Bolun Cai +2
Crowd counting on static images is a challenging problem due to scale variations. Recently deep neural networks have been shown to be effective in this task. However, existing neur…