Learning Spatio-Temporal Transformer for Visual Tracking

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

NVIDIA TESLA V100 GPU ARCHITECTURE

The NVIDIA Tesla V100 accelerator is the world’s highest performing parallel processor, designed to power the most computationally intensive HPC, AI, and graphics workloads. The GV100 GPU includes 21.1 billion transistors with a die size of 815 mm 2 .

  Tesla, V001, Tesla v100

NVIDIA A100 | Tensor Core GPU

NVIDIA V100 FP32 1X 6X BERT Large Training 1X 7X Up to 7X Higher Performance with Multi-Instance GPU (MIG) for AI Inference2 0 4,000 7,000 5,000 2,000 Sequences/second 3,000 NVIDIA A100 NVIDIA T4 1,000 6,000 BERT Large Inference 0.6X NVIDIA V100 1X

  Nvidia, V001, Nvidia v100

Number of parameters (M)

and batch=1 on a single Tesla V100. YOLOv3 baseline Our baseline adopts the architec-to YOLOv3-SPP in some papers [1,7]. We slightly change some training strategies compared to the orig-inal implementation [25], adding EMA weights updat-ing, cosine lr schedule, IoU loss and IoU-aware branch. We use BCE Loss for training cls and obj branch, reg ...

  Tesla, V001, Tesla v100

GPU Accelerator Capabilities

Tesla K80 Windows x64 Windows Server 2019 P40 Windows x64 Windows Server 2019 P100 Windows x64 Windows Server 2016 V100 Windows x64 Windows Server 2019 Linux x64 CentOS 7.7 NVIDIA Ampere A100 Linux x64 Red Hat 7.8 Quadro GV100 Windows x64 Windows 10 Linux x64 Red Hat 8.2 Tesla K80 Windows x64 Windows Server 2019 Linux x64 Red Hat 7.7

  Tesla, V001

GPU Computing Guide

Hardware Type NVIDIA Tesla V100 SXM 16GB NVIDIA Tesla V100 PCIe 16GB (for Servers) Min. CST version required 2018 SP 1 2018 SP 1 Number of GPUs 1 1 Max. Problem Size (Transient Solver) approx. 160 million mesh cells approx. 160 million mesh cells Form Factor Chip Passive Cooling Dual-Slot PCI-Express Passive Cooling Memory 16 GB CoWoS HBM2 16 ...

  Guide, Computing, Tesla, V001, Tesla v100, Gpu computing guide

Standard Edition, Version 1

v Overview Agisoft Metashape is a stand-alone software product that performs photogrammetric processing of digital images (aerial and close-range photography) and generates 3D spatial data to be used in GIS applications,

GPU Computing Guide

Tesla V100-PCIE-32GB 32 900 14 7 Tesla V100-SXM2-16GB 16 900 15 7.5 Tesla V100-PCIE-16GB 16 900 14 7 Tesla P100-SXM2 16 732 10.6 5.3 Tesla P100-PCIE-16GB 16 732 9.3 4.7 Tesla P100 16GB 16 732 9.3 4.7. 3DS.COM/SIMULIA c Dassault Systèmes GPU …

  Tesla, V001, Tesla v100