Mask R-CNN

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

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

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

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

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

17 Use Cases for Graph Databases and Graph Analytics - Oracle

They allow users to apply pattern recognition, classification, statistical analysis, and machine learning to these models, which enables ... detecting fraud based on datasets. For example, detection rules can be set up for: ... automobile company might have to issue a recall for a car model because .

  Oracle, Based, Model, Recognition, Detection, Marco del

Faster R-CNN: Towards Real-Time Object Detection with ...

cient, yet accurate, visual recognition. The OverFeat paper [18] computes conv features from an image pyramid for classification, localization, and detection. Adaptively-sized pooling (SPP) [7] on shared conv feature maps is proposed for efficient region-based object detection [7,16] and semantic segmentation [2].

  Based, Recognition, Detection

Rich Feature Hierarchies for Accurate Object Detection and ...

visual recognition tasks has been based considerably on the use of SIFT [26] and HOG [7]. But if we look at perfor-mance on the canonical visual recognition task, PASCAL VOC object detection [12], it is generally acknowledged that progress has been slow during 2010-2012, with small gains obtained by building ensemble systems and employ-

  Based, Recognition, Detection

Selective Search for Object Recognition

ing and recognizing objects based on their parts. They first generate a set of part hypotheses using a grouping method based on Arbeláez et al. (2011). Each part hypothesis is described by both appearance and shape features. Then, an object is recognized and carefully delineated by using its parts, achieving good results for shape recognition ...

  Based, Recognition

New Crossland - Vauxhall

Excludes fuel and insurance. Company Car Benefit in Kind (BiK) rates are monthly based on the standard paint price. On-the-road prices feature a VED based on preliminary CO 2 information. Effective 12 January 2022 New Crossland Model Year 2022B

  Based, Model, Crossland, New crossland, New crossland model

arXiv:2010.11929v2 [cs.CV] 3 Jun 2021

The model is trained in an unsu-pervised fashion as a generative model, and the resulting representation can then be fine-tuned or probed linearly for classification performance, achieving a maximal accuracy of 72% on ImageNet. Our work adds to the increasing collection of papers that explore image recognition at larger scales

  Model, Recognition

arXiv:1711.11585v2 [cs.CV] 20 Aug 2018

based on a perceptual loss [11,13,22] and produce the first model that can synthesize 2048 1024 images. The gen-erated results are high-resolution but often lack fine details and realistic textures. Motivated by their success, we show that using …

  Based, Model

Microsoft COCO: Common Objects in Context

Microsoft COCO: Common Objects in Context Tsung-Yi Lin 1, Michael Maire2, Serge Belongie , James Hays3, Pietro Perona2, Deva Ramanan4, Piotr Doll ar 5, C. Lawrence Zitnick 1Cornell, 2Caltech, 3Brown, 4UC Irvine, 5Microsoft Research Abstract. We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object

  Microsoft, Context, Common, Recognition, Object, Coco, Microsoft coco, Common objects in context

PATROL - www-asia.nissan-cdn.net

lanes and the car in your blind spot also continues to proceed, the system will initiate, gently braking one side of the vehicle, bringing your vehicle back to your original lane position, well out of harm's way. Ti-L model in Brilliant Silver. †Drivers’ aids only and should not be used as substitutes for safe driving practices.

  Model