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.

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

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

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

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-

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

SeqFormer: a Frustratingly Simple Model for Video Instance ...

ing vision task that aims to simultaneously perform detec-tion, classification, segmentation, and tracking of object in-stances in videos. Compared to image instance segmenta-tion [6], video instance segmentation is much more chal-lenging since it requires accurate tracking of objects across an entire video.

  Into, Detec, Detec tion

CNNs for Face Detection and Recognition

One of the most significant breakthrough on object detec-tion with Region Proposals is the R-CNN developed by Gir-shick et al. [9]. First R-CNN generates approximately 2000 Regions of Interest (RoI) using the Region Proposal method on the input image, then it warps each RoI into standard in-put size for the neural network and forward them into the

  Into, Faces, Recognition, Detection, Detec, Detec tion, For face detection and recognition

PATHFINDER

A lw a ys monitor tr af fic andc onditions and k eep both hands on the steering wheel .System oper ates onl y when lane ma rkings ar e detec ted .Does notnot functionfunc tion in al l w e ather ,traf fic and r oad c onditions. SystemSystem has limited

  Into, Detec

FSK: Signals and Demodulation - Rochester Institute of ...

Binary FSK (usually referred to simply as FSK) is a modula tion scheme typically used to send digital informa tion between digital equipment such as teleprinters and computers. The data are transmitted by shifting the frequency of a continuous carrier in ... at fA is rejected because it is out of the passband of the FM detec tor input filter. The

  Into, Signal, Detec, Signals and

ABSTRACT arXiv:1602.00763v2 [cs.CV] 7 Jul 2017

tion on a diverse set of testing sequences as set by the MOT benchmark database [6] which contains both moving and static camera sequences. For tuning the initial Kalman fil-ter covariances, IOU min, and T Lost parameters, we use the same training/validation split as reported in [12]. The detec-tion architecture used is the FrRCNN(VGG16) [22 ...

  Into, Detec, Detec tion

814.018 Ordinance on the Incentive Tax on Volatile Organic ...

Jan 01, 2018 · tion provisions in cases of measures taken to reduce emissions (Art. 9–9h); ... 2 The DETEC shall revise Annex 3 and the operating period under paragraph 1 letter b every five years after consulting the industries and cantons concerned. In doing so, it shall take account of technical developments.

  Incentives, Into, Ordinance, Detec, Ordinance on the incentive

XGBoost: A Scalable Tree Boosting System

tion for a given example is the sum of predictions from each tree. is for handling sparse data; a theoretically justi ed weighted quantile sketch procedure enables handling instance weights in approximate tree learning. Parallel and distributed com-puting makes learning faster which enables quicker model ex-ploration.

  System, Into, Tree, Boosting, Scalable, Xgboost, A scalable tree boosting system