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

Handout 4 Lattices in 1D, 2D, and 3D - Cornell University

• The volume (3D), area (2D), or length (1D) of a primitive cell can be given in terms of the primitive vectors, and is independent of the choice of the primitive vectors or of the primitive cells a1a2 3 a1. a2 a3 1 a1 1D 2D 3D Example, for the 2D lattice above: 2 …

  Lattice, 2d 3d, 2d lattice

3D Semantic Parsing of Large-Scale Indoor Spaces

a minimalistic floor plan by first triangulating the 2D floor plan and then merging adjacent segments to obtain the final space partitioning. Their approach does not handle occlu-sions effectively and requires the scan locations. Liu et al. [19] reconstructed a building in 3D given monocular images and the floor-plan.

  Large, Scale, Indoor, Semantics, Parsing, 3d semantic parsing of large scale indoor

ANSYS ICEM CFD Tutorial Manual - Purdue University College ...

Hexa Mesh Generation for a 2D Pipe Junction In this tutorial, you will generate a mesh for a two-dimensional pipe junction comprising two inlets and one outlet. After generating an initial mesh, you will check the quality of the mesh, and refine it for a …

  Cime

Frustum PointNets for 3D Object Detection From RGB-D Data

2D detector training are provided in the supplementary. 4.2. 3D Instance Segmentation Givena2Dimageregion(anditscorresponding3Dfrus-tum), several methods might be used to obtain 3D loca-tion of the object: One straightforward solution is to di-rectly regress 3D object locations (e.g., by 3D bounding box) from a depth map using 2D CNNs. However ...

Affine Transformations - Clemson University

into 3D vectors with identical (thus the term homogeneous) 3rd coordinates set to 1: " x y # =) 2 66 66 66 4 x y 1 3 77 77 77 5: By convention, we call this third coordinate the w coordinate, to distinguish it from the usual 3D z coordinate. We also extend our 2D matrices to 3D homogeneous form by appending an extra row and column, giving Scale ...

  Affine

Datasheet Quadro P600 - Nvidia

fast GPU memory enables the creation of complex 2D and 3D models and a flexible single-slot, low-profile form factor makes it compatible with even the most space and power-constrained chassis. Support for four 4K displays (4096x2160 @ 60Hz) with HDR color gives you an expansive visual workspace to view your creations in stunning detail.

  Quadro, Nvidia, P600, Quadro p600