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

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.

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

“L” Matching Networks - UC Santa Barbara

“L” Matching Networks 8 possibilities for single frequency (narrow-band) lumped element matching networks. Figure is from: G. Gonzalez, Microwave Transistor Amplifiers: Analysis and Design, Second Ed., Prentice Hall, 1997. These networks are used to cancel the reactive component of the load and transform the

  Network, Matching, Matching networks

Chapter 5 – Impedance Matching and Tuning - ITTC

3/12/2007 Matching Networks and Transmission Lines 1/7 Jim Stiles The Univ. of Kansas Dept. of EECS Matching Networks and Transmission Lines Recall that a primary purpose of a transmission line is to allow the transfer of power from a source to a load. Q: So, say we directly connect an arbitrary source to an

  Network, Matching, Matching networks

Impedance Matching - QSL.net

• The matching process becomes more difficult when real parts of the terminations are unequal, or when they have complex impedances. Example: Match a 50 Ω resistive source at 100MHz, to a 50 Ω resistive load that has in series a 1.59pF ... Impedance Matching using Resistor Networks

  Network, Matching

Matching Networks - University of California, Berkeley

The L-matching networks are designed as follows: 1 Calculate the boosting factor m = R hi R lo. 2 Compute the required circuit Q by (1 + Q2) = m, or Q = p m 1. 3 Pick the required reactance from the Q. If you’re boosting the resistance, e.g. R S > R L, then X s = Q R L. If you’re

  Network, Matching, Matching networks

Matching Networks for One Shot Learning - NeurIPS

Figure 1: Matching Networks architecture showing only a few examples per class, switching the task from minibatch to minibatch, much like how it will be tested when presented with a few examples of a new task. Besides our contributions in defining a model and training criterion amenable for one-shot learning,

  Network, Matching, Matching networks

Algorithms for Graph Similarity and Subgraph Matching

matching, which are both important practical problems useful in several fields of science, engineer-ing and data analysis. For the problem of graph similarity, we develop and test a new framework for solving the problem using belief propagation and …

  Matching, Algorithm, Graph, Similarity, Algorithms for graph similarity and subgraph matching, Subgraph

Abstract

matching and camera pose estimation tasks with indoor and outdoor datasets. The experiments show that LoFTR out-performs detector-based and detector-free feature matching baselines by a large margin. LoFTR also achieves state-of-the-art performance and ranks first among the published methods on two public benchmarks of visual localization.

  Matching

ABSTRACT arXiv:1409.1556v6 [cs.CV] 10 Apr 2015

arXiv:1409.1556v6 [cs.CV] 10 Apr 2015 Published as a conference paper at ICLR 2015 VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION Karen Simonyan∗ & Andrew Zisserman+ Visual Geometry Group, Department of Engineering Science, University of Oxford

  Network