MobileDets: Searching for Object Detection Architectures ...

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

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

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

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.

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

Using FFmpeg with NVIDIA GPU Hardware Acceleration

Using FFmpeg with NVIDIA GPU Hardware Acceleration vDA-08430-001_v02 | 1 Chapter 1. Introduction All NVIDIA® GPUs starting with Kepler generation support fully-accelerated hardware video encoding and decoding. The hardware encoder and hardware decoder are referred to as NVENC and NVDEC, respectively, in the rest of the document.

  Hardware, Nvidia, With, Acceleration, With nvidia gpu hardware acceleration

VMWARE HORIZON AND NVIDIA GRID VGPU

NVIDIA that enables a single GPU (graphics processing unit) to be shared among multiple virtual desktops. When NVIDIA GRID cards (installed in an x86 host) are used in a desktop and app virtualization solution running on VMware vSphere® 6.x, application graphics can be rendered with superior performance compared to non-hardware-accelerated ...

  Grid, Hardware, Nvidia, Unit, Processing, Horizons, Graphics, Vmware, Vgpu, Vmware horizon and nvidia grid vgpu, Graphics processing unit

NVIDIA QUADRO NVS 295 THE STANDARD FOR BUSINESS …

NVIDIA® PUREVIDEO™ TECHNOLOGY > Full-screen, full-frame video playback of HD and SD videos > ProcAmp Color Control Settings to correct for differences in RGB and TV monitors > Hardware color-space conversion (yUV 4:2:2 and 4:2:0) > IDCT Motion compensation > 5-tap horizontal by 3-tap vertical > MPEG-2 and WMV9 Decode Acceleration

  Hardware, Nvidia, Acceleration

NVIDIA A40 datasheet

The NVIDIA A40 GPU delivers state-of-the-art visual computing capabilities, including real-time ray tracing, AI acceleration, and multi-workload flexibility to accelerate deep learning, data science, and compute-based workloads. Virtual workstations powered by NVIDIA A40 and NVIDIA RTX Virtual Workstation (vWS) and NVIDIA Virtual Compute

  Datasheet, Nvidia, Acceleration, Nvidia a40 datasheet, Nvidia a40, Nvidia a40 gpu

NVIDIA A100 80GB PCIe GPU

The NVIDIA® A100 80GB PCIe card delivers unprecedented acceleration to power the world’s highest-performing elastic data centers for AI, data analytics, and high-performance computing (HPC) applications. NVIDIA A100 Tensor Core technology supports a broad range of math precisions, providing a single accelerator for every compute workload.

  Nvidia, Acceleration

ConnectX -5 EN Card 5 - NVIDIA

Mellanox ConnectX-5 Ethernet Adapter Card page 4 350 Oamead Paray Ste 100 Snnyale CA 405 Tel 40-0-3400 Fax 40-0-3403 www.mellanox.com Copright eano echnoogies rights resered eano eano ogo ConnectX irect eano ti-ost eano eerirect and 2. † † † †

  Nvidia

Eyeriss: A Spatial Architecture for Energy-Efficient ...

dataflows on various platforms, including GPU [14], FPGA [15–21], and ASIC [22–26]. However, due to dif-ferences in technology, hardware resources and system setup, a direct comparison between different implementations does not provide much insight into the relative energy efficiency of different dataflows. In this paper, we evaluate

  Hardware, Eyeriss

NVIDIA DGX A100 System Architecture

GPU incorporates 40 gigabytes (GB) of high-bandwidth HBM2 memory, larger and faster caches, and is designed to reduce AI and HPC software and programming complexity. Figure 2. NVIDIA A100 Tensor Core GPU The NVIDIA A100 GPU includes the following new features to further accelerate AI workload and HPC application performance.

  Nvidia