Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution (SISR). However, very deep can suffer from training difficulty and hardly achieve further gain. There are two main trends to solve that problem: improving the network architecture for better propagation of features through large number layers designing an attention mechanism selecting...

Video anomaly detection has gained significant attention in the current intelligent surveillance systems. We propose Deep Residual Spatiotemporal Translation Network (DR-STN), a novel unsupervised conditional Generative Adversarial (DR-cGAN) model with an Online Hard Negative Mining (OHNM) approach. The proposed DR-cGAN provides wider network to learn mapping from spatial temporal representatio...

Abstract Recently, a very deep convolutional neural network (CNN) has achieved impressive results in image super-resolution (SR). In particular, residual learning techniques are widely used. However, the previously proposed block can only extract one single-level semantic feature maps of single receptive field. Therefore, it is necessary to stack blocks higher-level maps, which will significant...