Relation-Aware Global Attention for Person Re-Identification

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

Understanding of Internal Clustering Validation Measures

Clustering validation, which evaluates the goodness of clustering results [1], has long been recognized as one of the vital issues essential to the success of clustering applications [2]. External clustering validation and internal clustering val-idation are the two main categories of clustering validation.

  External, Clustering, External clustering

AUTOSAR Layered Software Architecture

ICC clustering added. Document contents harmonized Legal disclaimer revised Release Notes added “Advice for users” revised “Revision Information” added 2006-11-28 2.1.1 AUTOSAR ... Standardized access to internal/external memory (non volatile memory)

  Architecture, Software, External, Clustering, Layered, Layered software architecture

AN OVERVIEW OF COMMON PARKING ISSUES PARKING …

External costs include increased road and parking facility costs, congestion, uncompensated accident damages, environmental degradation, ... Clustering parking. This layout can reduce the number of driveways onto arterials and can further improve traffic flow and safety, and create more accessible land use ...

  External, Clustering

FortiGate® 100E Series

§ Secure web access from both internal and external risks, even for encrypted traffic at high performance § Enhanced user experience with dynamic web and video ... High Availability Configurations Active / Active, Active / Passive, Clustering Dimensions and Power Height x Width x Length (inches) 1.75 x 17 x 10 1.75 x 17 x 10

  External, Fortigate, 100e, Clustering, 174 100e

CHAPTER 12 EXAMPLES: MONTE CARLO SIMULATION …

as a two-class model. In some situations, a special external Monte Carlo feature is needed to generate data by one model and analyze it by a different model. For example, variables can be generated using a clustered design and analyzed ignoring the clustering. Data generated

  Simulation, Example, External, Oracl, Monte, Clustering, Monte carlo simulation

Leveraging Machine Learning Within Anti-money ... - Accenture

regulators, external (to KYC) data sets can be used in areas such as marketing data, which has rich datasets on customer behavior not generally used for AML behavior modeling and alert enrichment and/or triage. However, there have been some developments in recent years that are paving the way to alleviate this challenge of data sharing.

  Accenture, External

Mapping Crime: Understanding Hot Spots - Office of Justice ...

About This Report Much of crime mapping is devoted to detecting high-crime-density areas known as hot spots. Hot spot analysis helps police identify high-crime areas, types of