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Single-Image Crowd Counting via Multi-Column …

Single-Image Crowd Counting via Multi-Column convolutional neural NetworkYingying Zhang Desen Zhou Siqin Chen Shenghua Gao Yi MaShanghaitech paper aims to develop a method than can accuratelyestimate the Crowd count from an individual image with ar-bitrary Crowd density and arbitrary perspective. To this end,we have proposed a simple but effective Multi-Column Con-volutional neural network (MCNN) architecture to map theimage to its Crowd density map. The proposed MCNN al-lows the input image to be of arbitrary size or utilizing filters with receptive fields of different sizes, thefeatures learned by each column CNN are adaptive to varia-tions in people/head size due to perspective effect or imageresolution.

2. Multi-column CNN for Crowd Counting 2.1. Density map based crowd counting To estimate the number of people in a given image via the Convolutional Neural Networks (CNNs), there are two natural configurations. One is a network whose input is the image and the output is the estimated head count. The other

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Image Style Transfer Using Convolutional Neural Networks

www.cv-foundation.org

Image Style Transfer Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of Tubingen, Germany¨ Bernstein Center for Computational Neuroscience, Tubingen, Germany¨

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Deep Convolutional Neural Fields for Depth Estimation From ...

www.cv-foundation.org

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Fully Convolutional Networks for Semantic Segmentation

www.cv-foundation.org

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolu-tional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmen-tation. Our key insight is to build “fully convolutional” networks that take input of arbitrary size and produce

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Convolutional Neural Networks at Constrained Time Cost

www.cv-foundation.org

Convolutional neural networks (CNNs) [15, 14] have re-cently brought in revolutions to the computer vision area. Deep CNNs not only have been continuously advancing the image classification accuracy [14, 21, 24, 1, 9, 22, 23], but also play as generic feature extractors for various recogni-tion tasks such as object detection [6, 9], semantic ...

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NTU RGB+D: A Large Scale Dataset for 3D Human Activity ...

www.cv-foundation.org

Multiview 3D event [43] and Northwestern-UCLA [40] datasets used more than one Kincect cameras at the same time to collect multi-view representations of the same ac-tion, and scale up the number of samples. It is worth mentioning, there are more than 40 datasets specifically for 3D human action recognition [47]. Al-

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Learning Spatiotemporal Features With 3D Convolutional ...

www.cv-foundation.org

the networks lose their input’s temporal signal after the first convolution layer. Only the Slow Fusion model in [18] uses 3D convolutions and averaging pooling in its first 3convo-lution layers. We believe this is the key reason why it per-forms best …

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Hierarchical Convolutional Features for Visual Tracking

www.cv-foundation.org

Visual representations are of great importance for object tracking. Numerous hand-crafted features have been used to represent the target appear-ance such as subspace representation [24] and color his-tograms [37]. The recent years have witnessed significant

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Predicting the Future Behavior of a Time-Varying ...

www.cv-foundation.org

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Deep Residual Learning for Image Recognition

www.cv-foundation.org

the residual learning principle is generic, and we expect that it is applicable in other vision and non-vision problems. 2. Related Work Residual Representations. In image recognition, VLAD [18] is a representation that encodes by the residual vectors with respect to a dictionary, and Fisher Vector [30] can be

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Unsupervised Visual Representation Learning by Context ...

www.cv-foundation.org

high-resolution natural images. Unsupervisedrepresentation learning can also be formu-lated as learning an embedding (i.e. a feature vector for each image) where images that are semantically similar are close, while semantically different ones are far apart. One way to build such a representation is to create a supervised

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openaccess.thecvf.com

we propose a multi-scale convolutional neural network that restores sharp images in an end-to-end manner where blur is caused by various sources. Together, we present multi-scale loss function that mimics conventional coarse-to-ne approaches. Furthermore, we propose a new large-scale dataset that provides pairs of realistic blurry image and the

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Spatial Pyramid Pooling in Deep Convolutional Networks …

tinman.cs.gsu.edu

Abstract—Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 224) input image. This require- ... new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. ... networks [3] cannot; 2) SPP uses multi-level spatial bins, while the sliding window pooling uses ...

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Multi-scale Residual Network for Image Super-Resolution

openaccess.thecvf.com

Keywords: Super-resolution · Convolutional neural network · Multi-scale residual network 1 Introduction Image super-resolution (SR), particularly single-image super-resolution (SISR), has attracted more and more attention in academia and industry. SISR aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) image

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A arXiv:1609.02907v4 [cs.LG] 22 Feb 2017

arxiv.org

In this section, we provide theoretical motivation for a specific graph-based neural network model f(X;A) that we will use in the rest of this paper. We consider a multi-layer Graph Convolutional Network (GCN) with the following layer-wise propagation rule: H(l+1) = ˙ D~ 1 2 A~D~ 1 2 H(l)W(l) : (2) Here, A~ = A+ I

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Notes on Convolutional Neural Networks - Cogprints

web-archive.southampton.ac.uk

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Depth Map Prediction from a Single Image using a Multi …

cs.nyu.edu

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Image Style Transfer Using Convolutional Neural Networks

www.cv-foundation.org

Generally each layer in the network defines a non-linear filter bank whose complexity increases with the position of the layer in the network. Hence a given input image ~x is encoded in each layer of the Convolutional Neural Network by the filter responses to that image. A layer with Nl dis-tinct filters has Nl feature maps each of size Ml ...

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Convolutional Sequence to Sequence Learning - arXiv

arxiv.org

by the decoder network to yield output element represen-tations that are being fed back into the decoder network g = ( g1;:::;gn). Position embeddings are useful in our architecture since they give our model a sense of which portion of the sequence in the input or output it is currently dealing with ( x5.4). 3.2. Convolutional Block Structure

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