Deformable 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.

Character-level Convolutional Networks for Text Classification

interest. We also insert 2 dropout [10] modules in between the 3 fully-connected layers to regularize. They have dropout probability of 0.5. Table 1 lists the configurations for convolutional layers, and table 2 lists the configurations for fully-connected (linear) layers. Table 1: Convolutional layers used in our experiments.

  Network, Fully, Convolutional, Convolutional networks

ImageNet Classification with Deep Convolutional Neural …

The output from the last 4096 fully-connected layer : 4096 dimensional feature. Discussion " Depth is really important. removing a single convolutional layer degrades the performance. ... Two-Stream Convolutional Networks for Action Recognition in Videos. NIPS 2014.

  Network, Fully, Convolutional, Convolutional networks

Spatial Pyramid Pooling in Deep Convolutional Networks …

1 Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun Abstract—Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 224) input image.This require-

  Network, Pyramid, Spatial, Convolutional, Convolutional networks, Pooling, Spatial pyramid pooling

1 DeepLab: Semantic Image Segmentation with Deep ...

A fully connected CRF is then applied to refine the segmentation result and better capture the object boundaries. segmentation-free approaches of [14], [52] directly apply DCNNs to the whole image in a fully convolutional fashion, transforming the last fully connected layers of the DCNN into convolutional layers. In order to deal with the ...

  With, Image, Deep, Fully, Segmentation, Convolutional, Semantics, Deeplab, Semantic image segmentation with deep, Fully convolutional

Convolutional Neural Networks for Facial Expression ...

on Convolutional Neural Networks (CNN) for facial expression recognition. The input into our system is an image; then, we use CNN to predict the facial ex- ... and fully connected layers, as well as the existance of batch normalization, dropout and max-pooling lay-ers. Along with dropout and batch normalization tech-

  Network, Expression, Fully, Neural, Convolutional, Facial, Convolutional neural networks for facial expression

Convolutional Neural Networks for Sentence Classification

Convolutional layer with multiple filter widths and feature maps Max -over -time pooling Fully connected layer with dropout and softmax output Figure 1: Model architecture with two channels for an example sentence. necessary) is represented as x 1: n = x 1 x 2::: x n; (1) is the concatenation operator. In gen-

  Network, Classification, Sentences, Fully, Neural, Convolutional, Convolutional neural networks for sentence classification

ImageNet Classification with Deep Convolutional Neural …

of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make train-ing faster, we used non-saturating neurons and a very efficient GPU implemen-tation of the convolution operation. To reduce overfitting in the fully-connected

  Fully, Convolutional

Iasonas Kokkinos arXiv:1412.7062v4 [cs.CV] 7 Jun 2016 ...

a first step to implement this, we convert the fully-connected layers of VGG-16 into convolutional ones and run the network in a convolutional fashion on the image at its original resolution. However this is not enough as it yields very sparsely computed detection scores (with a stride of 32 pixels). To

  Fully, Convolutional