nuScenes: A Multimodal Dataset for Autonomous Driving

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

Title: The development of autonomous vehicles

autonomous vehicles are still in a research and development phase, but numerous of powerful stakeholders are already forming partnerships in order to strengthen their position on future transportation markets. Based on theories of innovation and dominant design, the thesis will analyze the development of autonomous vehicles

  Development, Vehicle, Autonomous, Autonomous vehicles, The development of autonomous vehicles

Self-Driving Cars and Data Collection: Privacy Perceptions ...

autonomous vehicles operate there is little reasonable economists,expec­ tation of privacy and no notice or choice given, raising pri­ vacy questions. To improve the acceptance of networked au­ tonomous vehicles and to facilitate paper,the development of tech­ nological and policy mechanisms to protect privacy, public

  Development, Vehicle, Autonomous, Autonomous vehicles, The development

Automotive software and electronics 2030

deployed in vehicles, especially for ADAS, and with higher autonomous vehicle (AV) levels, i.e., AD. — ECUs will no longer be fully tied to the sourcing/development of SW functions. The change in business model is made possible by the separation of HW and SW.

  Development, Vehicle, Autonomous

The Future of the Last-Mile Ecosystem

development and implementation of effective interventions. In the first chapter, we describe the context of urban last-mile delivery, focusing on the unparalleled rise of e-commerce ... autonomous vehicles. The Future of the Last-Mile Ecosystem 7 Urbanization: Space as the scarcest resource

  Development, Vehicle, Autonomous, Autonomous vehicles

Autonomous Vehicle Implementation Predictions

autonomous vehicle development, and exaggerate future benefits. There is considerable uncertainty concerning autonomous vehicle development, benefits and costs, travel impacts, and consumer demand. Considerable progress is needed before autonomous vehicles can operate reliably in mixed urban traffic, heavy rain and snow, unpaved

  Development, Vehicle, Autonomous, Autonomous vehicles

Guidance, Navigation, and Control (GN&C)

development to ground testing and engineering support during the flight, Marshall makes sure the spacecraft gets where it’s going. The Center has a long history of designing, developing and testing GN&C systems for launch vehicles, satellites, landers, rovers, and space telescopes, including Shuttle, X-33, Ares, Chandra, and Hubble.

  Development, Control, Vehicle, Guidance, Navigation, And control

Electric road vehicles in the European Union

They were among the first vehicles in the 1800s, and by 1900 accounted for around a third of all vehicles on the road. battery and electrical However, network weaknesses and the rise of the cheap oil era , together with the technological advancement of diesel and petrol vehicles , shifted interest away from electric vehicles for many decades.

  Vehicle