Single-Image Crowd Counting via Multi-Column …
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
Link to this page:
Documents from same domain
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¨
volutional neural networks (CNN). CNN features have been setting new records for a wide variety of vision applica-tions . Despite all the successes in classiﬁcation prob-
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
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 ...
Multiview 3D event  and Northwestern-UCLA  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 . Al-
the networks lose their input’s temporal signal after the ﬁrst convolution layer. Only the Slow Fusion model in  uses 3D convolutions and averaging pooling in its ﬁrst 3convo-lution layers. We believe this is the key reason why it per-forms best …
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  and color his-tograms . The recent years have witnessed signiﬁcant
Predicting the Future Behavior of a Time-Varying Probability Distribution Christoph H. Lampert IST Austria firstname.lastname@example.org Abstract We study the problem of predicting the future, though
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  is a representation that encodes by the residual vectors with respect to a dictionary, and Fisher Vector  can be
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
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
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  cannot; 2) SPP uses multi-level spatial bins, while the sliding window pooling uses ...
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
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
Convolutional neural networks in-volve many more connections than weights; the architecture itself realizes a form of regularization. In addition, a convolutional network automatically provides some degree of translation invariance. This particular kind of neural network assumes that we wish to learn ﬁlters, in a data-driven fash-
In this way, the local network can edit the global prediction to incorporate ﬁner-scale details. 3.1.1 Global Coarse-Scale Network The task of the coarse-scale network is to predict the overall depth map structure using a global view of the scene. The upper layers of this network are fully connected, and thus contain the entire image
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 ...
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