Occupancy Networks: Learning 3D Reconstruction in Function ...

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 [email protected] [email protected] [email protected] 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 [email protected] [email protected]

  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

Building Code Title 27 Subchapter 3 - New York City

Jul 01, 2008 · [307.3] 256 Occupancy Group F-2 [307.4] 257 Occupancy Group F-3 [307.5] 258 Occupancy Group F-4 [308.0] Art. 9 Occupancy Group G-Educational [308.1] 259 Classification [309.0] Art. 10 Occupancy Group H-Institutional [309.1] 260 Classification ...

  York, Title, Group, New york city, City, Subchapter, Occupancy, Group occupancy, Group f occupancy, Title 27 subchapter 3

CHAPTER 3 USE AND OCCUPANCY CLASSIFICATION

FACTORY GROUP F 306.1 Factory Industrial Group F. Factory Industrial Group F occupancy includes, among others, the use of a building or structure, or a portion thereof, for assembling, disassembling, fabricating,finishing,manufacturing,packaging,repairorpro-cessing operations that are not classified as a Group H hazard-ous or Group S storage ...

  Group, Occupancy, Group f, Group f occupancy

Building Code Title 27 Subchapter 6 - New York City

Oct 01, 2004 · (a) Mixed occupancy. - When a building is classified in more than one occupancy group in accordance with the provisions of section 27-239 of article two of subchapter three of this chapter, the exit requirements for the entire building shall be determined on the basis of the occupancy group having the strictest exit requirements, or the exit

  York, Group, New york city, City, Occupancy, Group occupancy

IBC 2009, NJ Ed. -Chapter 3, Use and Occupancy ...

IBC 2009, NJ Ed. -Chapter 3, Use and Occupancy Classification Group F-1 Factory F-2 Factory H-1 High Hazard H-2 High Hazard H-3 High Hazard H-4 High Hazard H-5 High Hazard I-1 Institutional I-2 Institutional Group I-2 EXCEPTIONS: I-3 Institutional Description Factory industrial uses of a building or structure, or a portion thereof, for assembling, disassembling, …

  Group, Occupancy, Group f

Vive PowPak Fixture Controls

occupancy sensor or Radio Powr Savr daylight sensor to group and control more than one fixture together. • Radio Powr Savr occupancy sensors can be used with the PowPak fixture sensor to add coverage area. Wired: • Maximum of 1 PowPak fixture sensor. Note: Only 1 PowPak wireless fixture sensor can be wired per PowPak fixture control.

  Group, Occupancy

TABLE OF CONTENTS - Energy

adding occupancy sensors to turn lights down or off during low activity periods, consider whether to turn off individual luminaires or reduce output for a group of luminaires. Turning off luminaires may result in poorer lighting uniformity on the pave-ment and increased shadows that may make occupants feel less safe. However, some

  Group, Occupancy

Building Plan Review Cover Sheet - Fairfax County, Virginia

Occupancy/Group: R-5 R-3 . RESIDENTIAL . Case number: Yes No; if yes: Zoning application #: 3) 2) 1) Condition reference Drawing # development conditions, special permits, variances . Conditions: list below building-related proffers, Green Building . If yes, specify below criteria existing R-5 residential construction)

  Virginia, County, Group, Occupancy, Fairfax, Fairfax county

Affirmative Fair Housing OMB Approval No. 2529 Marketing ...

approved for occupancy in the subject project, in accordance with the contract, grant, etc. Block 2c- Respondents should specify the date the project was/will be first occupied. Block 2d- For new construction and substantial rehabilitation projects, advertising must begin at least 90 days prior to initial occupancy. In the case of existing

  Occupancy

COMMERCIAL FLEXIBLE SPRINKLERS - Viking Group Inc

The “F” designates high flow rate using 1 1/4” internal hose diameter. FlexHead products are intended for use in hydraulically designed wet, pre-action, deluge or dry pipe sprinkler connections per NFPA 13, 13R, and 13D guidelines.

  Group

2018 Form 990-EZ

F . Group Exemption Number . . G . Accounting Method: Cash. Accrual Other (specify) . H . Check if the organization is . not . required to attach Schedule B (Form 990, 990-EZ, or 990-PF). I Website: . J Tax-exempt status (check only one) — 501(c)(3) 501(c) (insert no.) 4947(a)(1) or . 527 K . Form of organization: Corporation. Trust. Association

  Group