GIRAFFE: Representing Scenes As Compositional Generative ...

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

CartoonGAN: Generative Adversarial Networks for Photo ...

2.1. Non­photorealistic rendering (NPR) Many NPR algorithms have been developed, either au-tomatically or semi-automatically, to mimic specific artis-tic styles including cartoons [25]. Some works render 3D shapes in simple shading, which creates cartoon-like ef-fect [28]. Such techniques called cel shading can save

  Technique, Rendering, Photorealistic, Photorealistic rendering

Revit MEP basics - ibse.hk

• Navisworks* & rendering** • Formatting schedules • Coordination with other file types (e.g. IFC, DWG, DXF, SAT) • Coordination models & clash detection • Quick review of models for non-CAD users • Rendering & animations • Create a photorealistic image of the building model • Lighting simulation & raytracing technique

  Rendering, Photorealistic

Photo Tourism: Exploring Photo Collections in 3D

we present a novel non-photorealistic rendering technique that pro-vides a better sense of scene appearance. While standard 3D navigation controls are useful for some in-teractions, they are not ideal for other tasks. For example, suppose you want to see images of a particular object or region in the scene.

  Rendering, Photorealistic, Photorealistic rendering

ffirstname.lastnameg@tue.mpg.de arXiv:2011.12100v2 [cs.CV ...

works [50,51,66,81,92] propose differentiable rendering techniques. Mildenhall et al. [61] propose Neural Radiance Fields (NeRFs) in which they combine an implicit neural model with volume rendering for novel view synthesis of complex scenes. Due to their expressiveness, we use a generative variant of NeRFs as our object-level represen-tation.

  Technique, Rendering, Rendering techniques

arXiv:2012.02190v3 [cs.CV] 30 May 2021

ing conventional volume rendering techniques. This frame-work is illustrated in Fig.2. PixelNeRF has many desirable properties for few-view novel-view synthesis. First, pixelNeRF can be trained on a dataset of multi-view images without additional supervision such as ground truth 3D shape or object masks. Second,

  Technique, Rendering, Rendering techniques

A STUDY PATH FOR - GitHub Pages

Rendering (2012) Polygon Mesh Processing (2010) 3D Engine Design for Virtual Globes (2011) Isosurfaces (2013) Non-Photorealistic Computer Graphics (2002) Graphics Gems II (1991) Graphics Gems III (1994) Graphics Gems IV (1994) Graphics Gems V (1995) Jim Blinn's Corner: Dirty Pixels (1998) Andrew Glassner's Notebook (1999) Jim Blinn's Corner ...

  Rendering, Photorealistic

Image Style Transfer Using Convolutional Neural Networks

Image Style Transfer Using Convolutional Neural Networks Leon A. Gatys Centre for Integrative Neuroscience, University of Tubingen, Germany¨ Bernstein Center for Computational Neuroscience, Tubingen, Germany¨

  Styles, Transfer, Style transfer

Introduction to Computer Graphics Techniques and …

• Techniques: contour plots, color coding, constant value surface ... – Creating photorealistic rendering images. 45 Basic Topics – Undergraduate • Geometric models – Curves, surfaces, solids – Polygonal models – Parametric representations – Implicit representations

  Technique, Rendering, Photorealistic, Photorealistic rendering