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

5 Geometric Modeling.ppt - UVic.ca

Summary - Geometric Modeling • Geometric Modeling is a fundamental CAD technique. • The capability of various CAD tools in geometric modeling is usually used as a crucial factor in tool selectionusually used as a crucial factor in tool selection. • Wireframe models consist entirely of points, lines, and curves.

  Modeling, Geometric, 5 geometric modeling, Geometric modeling

As-Rigid-As-Possible Surface Modeling - Olga Sorkine-Hornung

face deformations induced by modeling operations are close to rigid, surface details tend to be preserved. This is a highly important property for surface editing schemes that are meant to be applied to complex, detailed surfaces, such as those coming from scanning real 3D objects or from sophis-ticated virtual sculpting tools.

  Modeling

Building Information Modeling Execution Planning Guide

Building Information Modeling Execution Planning Guide ©2010 The Computer Integrated Construction Research Group The Pennsylvania State University i EXECUTIVE SUMMARY A Building Information Model (IM) is ^a digital representation of physical and functional characteristics of a facility. _1 To successfully implement BIM, a project team must perform …

  Modeling

AutoCAD Civil 3D Manual - Geo-Tiff.com

May 30, 2013 · The ‘3D Modeling’ and ‘Planning and Analysis’ workspaces are used mostly in the Construction and Engineering industries ; Geomatics technicians will use them on an as needed basis. Workspaces can be switched at any point during the project: 1. Left-click on the cog icon on the status bar in the bottom right hand corner.

  Manual, Modeling, Autocad, Civil, 3d modeling, Autocad civil 3d manual

Photo Tourism: Exploring Photo Collections in 3D

3D interface. Our system consists of an image-based modeling front end that automatically computes the viewpoint of each photo-graph as well as a sparse 3D model of the scene and image to model correspondences. Our photo explorer uses image-based rendering techniques to smoothly transition between photographs, while also

  Modeling

Learning Blender: A Hands-On Guide to Creating 3D …

and edit in 3D using Blender, the free open-source application. Learning to work in 3D can be tough, and Villar uses characters to teach many different techniques, in-cluding modeling, lighting, shading, rigging, and animation. The book is filled with great tips and tricks, and can help anyone learn how to work in 3D.”

  Modeling, Blender

PointNet: Deep Learning on Point Sets for 3D Classification ...

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Qi* Hao Su* Kaichun Mo Leonidas J. Guibas Stanford University Abstract Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images.