Learning Continuous Image Representation With Local ...

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

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

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

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

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.

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

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

Cultural Diversity at Work: The Effects of Diversity ...

representation of certain demographic groups influences those traditionally in the minority. The second involves research on the effects of group composition on outcomes related to work group effectiveness. Effects of proportional representation. Much of the litera- ture on proportional representation has focused on the

  Work, Representation

Strategies that Work for Students with Autism Spectrum ...

Multiple means of representation ... Group Work Seed Discussion –After writing ideas, discuss . Partners Prediction Relay strategy allows each student to (1) make predictions about the assigned text, (2) take turns reading for 5 minutes, (3) check their predictions, and

  Work, Representation, Of representation

Message from the National Adjutant - DAV

Throughout our history, DAV’s main goal has been to provide the best, most professional claims representation to all injured and ill veterans and their families and survivors. An integral part of that goal is fielding a ... DAV was chartered by Congress in 1932 to work for the physical, mental, social and economic

  Work, Representation

DeepSDF: Learning Continuous Signed Distance Functions for ...

pression capabilities. In this work, we introduce DeepSDF, a learned continuous Signed Distance Function (SDF) rep-resentation of a class of shapes that enables high qual-ity shape representation, interpolation and completion from partial and noisy 3D input data. DeepSDF, like its clas-sical counterpart, represents a shape’s surface by a con-

  Work, Representation, Enestration, Rep resentation

Provide multiple means of Engagement Representation …

The UDL Guidelines are set up in columns (principles, left to right: engagement, representation, action & expression) and rows (top to bottom: access, build, internalize) with the goal of UDL (expert learners). The "access" row of the UDL Guidelines includes: Provide options for recruiting interest (engagement), Provide options for perception ...

  Guidelines, Representation, Udl guidelines

GALOIS THEORY AT WORK: CONCRETE EXAMPLES

GALOIS THEORY AT WORK: CONCRETE EXAMPLES 3 Remark 1.3. While Galois theory provides the most systematic method to nd intermedi-ate elds, it may be possible to argue in other ways. For example, suppose Q ˆFˆQ(4 p 2) with [F: Q] = 2. Then 4 p 2 has degree 2 over F. Since 4 p 2 is a root of X4 2, its minimal polynomial over Fhas to be a ...

  Work, Galois

CHAPTER 2 Data Representation in Computer Systems

2.4 Signed Integer Representation 54 • By convention, a “1” in the high-order bit indicate a negative number. 2.4.1 Signed Magnitude 54 • A signed-magnitude number has a sign as its left-most bit (also referred to as the high-order bit or the most significant bit) while the remaining bits represent the magnitude (or absolute value

  Representation

2019-2020 REPORT

need for action on climate change, to a deep digital divide and lack of representation and inclusion in our technology industry, to the reality that the current pandemic demands new thinking about global health challenges we will face together in the future. Intel has a long history of leadership in corporate responsibility.

  Representation, Of representation

Theme Park - Home - Education Bureau

viii Contents Page 1. The fundamental concepts of theme parks 10 1.1 Types of theme park 12 1.2 The features of a theme park 17 2. Factors contributing to the success of theme parks 21 2.1 Overall attractiveness and visitors’ experiences 21