Selective Kernel 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

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

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

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

C Programming Tutorial - Mark Burgess

of the raw computer is a stream of numbers called machine code. As you might expect, the action which results from a single machine code instruc-tion is very primitive and many thousands of them are required to make a program which does anything substantial. ... it has a set of very flexible loop construc-4 Chapter 1: Introduction tions (for ...

  Programming, Code, Into, Tutorials, C programming tutorial, Construc

Regulation 61-25 Retail Food Establishments

May 27, 1983 · (12) “CFR” means Code of Federal Regulations. Citations in this regulation to the CFR refer sequentially to the Title, Part, and Section numbers such as 40CFR 180.194 refers to Title 40, Part 180, Section 194.

  Code

Chapter 3: Occupancy Classification and Use

tion, allowable heights, and areas and types of construc-tion becomes apparent. Respective to each occupancy classification, the greater the fire-resistance rating of structural elements, as represented by the type of con-struction, the greater the floor area and height allow-ances. The greater the potential fire hazards indicated

  Chapter, Into, Classification, Chapter 3, Occupancy, Use and occupancy classification, Construc

WCD1 - Details for Conventional Wood Frame Construction

be limited by building code requirements in use where the building is being constructed. Conventional construc-tion provisions, as found in this publication, represent techniques with a history of satisfactory performance. Today, some building codes may require a more rigor-ous structural design methodology than is associated with

  Code, Into, Construc

UNIFORM CONSTRUCTION CODE STATUTE

wealth is to adopt a Uniform Construction Code. (4) The model code of the Building Of ficials and Code Administrators International, Inc. (BOCA), is a construc-tion code which has been widely adopted in this Commonwealth and in the geographical region of the United States of which this Commonwealth is a part.

  Code, Construction, Into, Construction code, Construc, Construc tion code

C o n d itio n a l C o m m itm e n t D ire c t E n d o rs ...

C . Warranty: Form HUD-92544 is required on all new construc-tion and shall be executed between the builder and the p u rch a se r. D . Section 223: This commitment is issued pursuant to Section 2 2 3 (e ). E . H e a lth A u th o rity A p p ro v a l: S u b m it lo ca l h e a lth authority

  Into, Construc