DeepEMD: Few-Shot Image Classification With …

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

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

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

Object Detection and Tracking using Deep Learning and ...

vehicle dataset for single object detection and YOLOv3 for multiple object detection on KITTI and COCO dataset. Model performance is analyzed, evaluated and tabulated using performance metrics such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Accuracy,

  Performance, Using, Multiple, Tracking, Metrics, Object, Detection, Performance metrics, Object detection and tracking using, Multiple object

JOURNAL OF LA FairMOT: On the Fairness of Detection and …

JOURNAL OF LATEX CLASS FILES 1 FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking Yifu Zhang , Chunyu Wang , Xinggang Wangy, Wenjun Zeng, Wenyu Liu Abstract—There has been remarkable progress on object detection and re-identification (re-ID) in recent years which are the key components of multi-object …

  Multiple, Tracking, Object, Multiple object tracking

Accelerating Innovation Through a Standard Chiplet Interface

all in a single package while keeping performance and power close to what would be possible if they were all monolithic. That challenge is being met with the combination of the Advanced Interface Bus (AIB) with packaging technologies that allow heterogeneous integration of multiple die into a single package. The AIB interconnect scheme, recently

  Performance, Innovation, Multiple, Through, Accelerating, Accelerating innovation through

GIT for Beginners (handout)

You may have multiple variants of the same software, materialised as branches, for example: •a main branch •a maintainance branch (to provide bugfixes in older releases) •a development branch (to make disruptive changes) •a release branch (to freeze code before a new release) VC tools will help you to: •handle multiple branches ...

  Multiple, Beginner, Handouts, Git for beginners

INTRODUCTION TO MACHINE VISION - Assembly Mag

machine controller, triggering a reject mechanism that ejects the object from the line. In practice, a fixed-mount camera captures images of parts as they pass the camera’s field of view and the system uses software to calculate distances between various points in the image. Because many machine vision systems can measure object features to

  Introduction, Machine, Object, Vision, Introduction to machine vision

Licensing Program Guide - Commvault

Object store, CIFS/NFS direct access Analyze Commvault backup data Analyze live data sources File dashboards: storage distribution, tree size, orphaned files, duplicate files, inactive users, security permissions

  Object, Commvault

NVIDIA | GPU Applications Catalog

the performance of Financial applications (e.g. Monte-Carlo, Finite-difference) with minimum changes to existing code. • C++ programming language, cross-platform (back-end generates CUDA and optimized CPU code) • Supports Windows and Linux operating systems Multi-GPU Single Node hpc-gpu-apps-catalog-2028673-r3-print.indd 1 11/9/21 3:40 PM

  Performance

Get Started with Sales Cloud Implementation Guide

INTRODUCTION Welcome to Salesforce With Salesforce, you can work smarter, connect better with customers, sell more, and grow your business faster.