Attention Augmented Convolutional Networks

Squeeze-and-Excitation Networks - openaccess.thecvf.com

Squeeze-and-Excitation Networks Jie Hu1∗ Li Shen2∗ Gang Sun1 hujie@momenta.ai lishen@robots.ox.ac.uk sungang@momenta.ai 1 Momenta 2 Department of Engineering Science, University of Oxford Abstract Convolutional neural networks are built upon the con-

  Network, Excitation, Squeeze and excitation networks, Squeeze

Class-Balanced Loss Based on Effective Number of Samples

and large-scale datasets including ImageNet and iNatural-ist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve signifi-cant performance gains on long-tailed datasets. 1. Introduction The recent success of deep Convolutional Neural Net-works (CNNs) for visual recognition [26, 37, 38, 16] owes

  Scale, Visual, Recognition, Visual recognition

ESRGAN: Enhanced Super-Resolution Generative Adversarial ...

ESRGAN: EnhancedSuper-Resolution Generative Adversarial Networks Xintao Wang 1, Ke Yu , Shixiang Wu2, Jinjin Gu3, Yihao Liu4, Chao Dong 2, Yu Qiao , and Chen Change Loy5 1 CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong 2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 3 The Chinese University of Hong Kong, …

  Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks

What Have We Learned From Deep Representations for …

what these powerful models actually have learned. In this paper we shed light on deep spatiotemporal net-works by visualizing what excites the learned models us-ing activation maximization by backpropagating on the in-put. We are the first to visualize the hierarchical features

  What, Have, Learned, What we have learned, Have learned

Finding Tiny Faces in the Wild With Generative Adversarial ...

faces, which are unfriendly for the face classifier. Toward-s this end, we design a refinement sub-network to recover some detailed information. In the discriminator network, the basic GAN [17, 12, 8] is trained to distinguish the real and fake high resolution images. To classify faces or non-

  Faces

RegularFace: Deep Face Recognition via Exclusive ...

RegularFace: Deep Face Recognition via Exclusive Regularization Kai Zhao Jingyi Xu Ming-Ming Cheng ∗ TKLNDST, CS, Nankai University kaiz.xyz@gmail.com cmm@nankai.edu.cn

  Zhao

Protecting World Leaders Against Deep Fakes

Protecting World Leaders Against Deep Fakes Shruti Agarwal and Hany Farid University of California, Berkeley Berkeley CA, USA {shrutiagarwal, hfarid}@berkeley.edu

Auto-DeepLab: Hierarchical Neural Architecture Search for ...

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation Chenxi Liu1∗, Liang-Chieh Chen 2, Florian Schroff2, Hartwig Adam2, Wei Hua2, Alan Yuille1, Li Fei-Fei3 1Johns Hopkins University 2Google 3Stanford University Abstract Recently, NeuralArchitectureSearch(NAS)hassuccess-

PointNet: Deep Learning on Point Sets ... - CVF Open Access

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Qi* Hao Su* Kaichun Mo Leonidas J. Guibas Stanford University

  Open, Learning, Points, Deep, Sets, Pointnet, Deep learning on point sets

Frustum PointNets for 3D Object Detection From RGB-D Data

Frustum PointNets for 3D Object Detection from RGB-D Data Charles R. Qi1∗ Wei Liu2 Chenxia Wu2 Hao Su3 Leonidas J. Guibas1 1Stanford University 2Nuro, Inc. 3UC San Diego Abstract In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.

Spatio-Temporal Graph Convolutional Networks: A Deep ...

graph 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.

  Network, Tional, Graph, Convolutional, Temporal, Convolutional networks, Positas, Spatio temporal graph convolutional networks, Convolu

Siamese Neural Networks for One-shot Image Recognition

Several 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

  Network, Tional, Convolutional, Convolutional networks, Convolu

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 ...

  Network, Tional, Convolutional, Convolutional networks, Convolu tional networks, Convolu

Dilated Residual Networks - CVF Open Access

Convolutional 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 ...

  Network, Tional, Residual, Convolutional, Convolutional networks, Dilated, Convos, Convolu tional networks, Convolu, Dilated residual networks, Lutional

Abstract arXiv:1411.4038v2 [cs.CV] 8 Mar 2015

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 ...

  Network, Tional, Convolu tional networks, Convolu

Image Restoration Using Very Deep Convolutional Encoder ...

The 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.

  Using, Tional, Deep, Restoration, Very, Convolutional, Convolu, Restoration using very deep convolutional

Convolutional Neural Networks

but 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-

  Network, Tional, Convolu

Learning Spatiotemporal Features With 3D Convolutional ...

few 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 …

  Tional, Convolu