25 research outputs found
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
Methods based on convolutional neural network (CNN) have demonstrated
tremendous improvements on single image super-resolution. However, the previous
methods mainly restore images from one single area in the low resolution (LR)
input, which limits the flexibility of models to infer various scales of
details for high resolution (HR) output. Moreover, most of them train a
specific model for each up-scale factor. In this paper, we propose a
multi-scale super resolution (MSSR) network. Our network consists of
multi-scale paths to make the HR inference, which can learn to synthesize
features from different scales. This property helps reconstruct various kinds
of regions in HR images. In addition, only one single model is needed for
multiple up-scale factors, which is more efficient without loss of restoration
quality. Experiments on four public datasets demonstrate that the proposed
method achieved state-of-the-art performance with fast speed
Bayesian Multi Scale Neural Network for Crowd Counting
Crowd Counting is a difficult but important problem in computer vision.
Convolutional Neural Networks based on estimating the density map over the
image has been highly successful in this domain. However dense crowd counting
remains an open problem because of severe occlusion and perspective view in
which people can be present at various sizes. In this work, we propose a new
network which uses a ResNet based feature extractor, downsampling block which
uses dilated convolutions and upsampling block using transposed convolutions.
We present a novel aggregation module which makes our network robust to the
perspective view problem. We present the optimization details, loss functions
and the algorithm used in our work. On evaluating on ShanghaiTech, UCF-CC-50
and UCF-QNRF datasets using MSE and MAE as evaluation metrics, our network
outperforms previous state of the art approaches while giving uncertainty
estimates in a principled bayesian manner.Comment: 10 page
CountNet: End to End Deep Learning for Crowd Counting
We approach crowd counting problem as a complex end to end deep learning process that needs both a correct recognition and counting. This paper redefines the crowd counting process to be a counting process, rather than just a recognition process as previously defined. Xception Network is used in the CountNet and layered again with fully connected layers. The Xception Network pre-trained parameter is used as transfer learning to be trained again with the fully connected layers. CountNet then achieved a better crowd counting performance by training it with augmented dataset that robust to scale and slice variations