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Fully Convolutional Networks for Semantic Segmentation
networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolu-tional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet ...
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Image Style Transfer Using Convolutional Neural Networks
www.cv-foundation.orgImage Style Transfer Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of Tubingen, Germany¨ Bernstein Center for Computational Neuroscience, Tubingen, Germany¨
Learning Spatiotemporal Features With 3D Convolutional ...
www.cv-foundation.orgthe 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 …
Deep Convolutional Neural Fields for Depth Estimation From ...
www.cv-foundation.orgvolutional neural networks (CNN). CNN features have been setting new records for a wide variety of vision applica-tions [13]. Despite all the successes in classification prob-
Convolutional Neural Networks at Constrained Time Cost
www.cv-foundation.orgConvolutional 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 ...
Single-Image Crowd Counting via Multi-Column …
www.cv-foundation.orgSingle-Image Crowd Counting via Multi-Column Convolutional Neural Network Yingying Zhang Desen Zhou Siqin Chen Shenghua Gao Yi Ma Shanghaitech University {zhangyy2,zhouds,chensq,gaoshh,mayi}@shanghaitech.edu.cn Abstract ... column CNN is adaptive to (hence the overall network
Hierarchical Convolutional Features for Visual Tracking
www.cv-foundation.orgVisual 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
Predicting the Future Behavior of a Time-Varying ...
www.cv-foundation.orgPredicting the Future Behavior of a Time-Varying Probability Distribution Christoph H. Lampert IST Austria chl@ist.ac.at Abstract We study the problem of predicting the future, though
Deep Residual Learning for Image Recognition
www.cv-foundation.orgthe 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
NTU RGB+D: A Large Scale Dataset for 3D Human Activity ...
www.cv-foundation.orgMultiview 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-
Unsupervised Visual Representation Learning by Context ...
www.cv-foundation.orghigh-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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Spatio-Temporal Graph Convolutional Networks: A Deep ...
www.ijcai.orggraph convolutional networks, for trafÞc forecasting tasks. This architecture comprises several spatio-temporal convolu-tional blocks, which are a combination of graph convolutional layers[Defferrardet al., 2016] and convolutional sequence learning layers, to model spatial and temporal dependencies.
Attention Augmented Convolutional Networks
openaccess.thecvf.comAttention Augmented Convolutional Networks Irwan Bello Barret Zoph Ashish Vaswani Jonathon Shlens Quoc V. Le Google Brain {ibello,barretzoph,avaswani,shlens,qvl}@google.com Abstract Convolutional networks have been the paradigm of choice in many computer vision applications. The convolu-tion operation however has a significant weakness in that it
Siamese Neural Networks for One-shot Image Recognition
www.cs.cmu.eduSeveral factors make convolutional networks especially ap-pealing. Local connectivity can greatly reduce the num-ber of parameters in the model, which inherently provides some form of built-in regularization, although convolu-tional layers are computationally more expensive than stan-dard nonlinearities. Also, the convolution operation used in
Dilated Residual Networks - CVF Open Access
openaccess.thecvf.comConvolutional networks were originally developed for classifying hand-written digits [9]. More recently, convolu-tional network architectures have evolved to classify much more complex images [8, 13, 14, 6]. Yet a central aspect of network architecture has remained largely in place. Convo-lutional networks for image classification ...
![Abstract arXiv:1411.4038v2 [cs.CV] 8 Mar 2015](/cache/preview/e/8/a/1/0/5/9/a/thumb-e8a1059a2748cde7ba3d756f44e4ca34.jpg)
Abstract arXiv:1411.4038v2 [cs.CV] 8 Mar 2015
arxiv.orgnetworks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolu-tional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet ...
Image Restoration Using Very Deep Convolutional Encoder ...
proceedings.neurips.ccThe convolu-tional layers act as the feature extractor which encode the primary components of image contents while eliminating the corruption. The deconvolutional layers then decode the image abstraction to recover the image content details. We propose to add skip connections between corresponding convolutional and deconvolu-tional layers.
Convolutional Neural Networks
proceedings.mlr.pressbut very effective attention module for Convolu-tional Neural Networks (ConvNets). In contrast to existing channel-wise and spatial-wise attention modules, our module instead infers 3-D atten-tion weights for the feature map in a layer with-out adding parameters to the original networks. Specifically, we base on some well-known neuro-
Learning Spatiotemporal Features With 3D Convolutional ...
www.cv-foundation.orgfew years in feature learning, various pre-trained convolu-tional network (ConvNet) models [16] are made available for extracting image features. These features are the activa-tions of the network’s last few fully-connected layers which perform well on …