Digging Into Self-Supervised Monocular Depth Estimation

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

Image Signal Processor

16Mega Pixel Image Signal Processor THP7312 OV13850-13MP Camera Module LED Board Image Sensor Board THine® ISP Board Cypress® CX3™ Board USB 3.0 Debug Port Interface Overview

  Image

Medium Format vs 135mm - EJPhoto.com

the 35mm lens on the Phase One due to the aspect ratio difference between the two sensors. Careful examination also reveals slightly more contrast in the Sony image than the Phase One

  Medium, Image, Format, Sensor, Medium format vs

CMOS Image Sensors - Harvest Imaging

CMOS Image Sensors : State-Of-The-Art and Future Perspectives Albert THEUWISSEN DALSA Semiconductors, Eindhoven, The Netherlands Delft University of Technology, Delft, The Netherlands

  Image, Sensor, Image sensors

Solid-State Electronics - Harvest Imaging

CMOS image sensors: State-of-the-art Albert J.P. Theuwissen* Harvest Imaging, Kleine Schoolstraat, 9, B-3960 Bree, Belgium Delft University of Technology, Mekelweg, 4, 2628 CD Delft, The Netherlands

  States, Image, Electronic, Solid, Sensor, Image sensors, Solid state electronics

Oracle Spatial 11g GeoRaster

White Paper Title Page 4 Oracle Spatial 11g GeoRaster 1 ORACLE SPATIAL GEORASTER 1.1 Introduction GeoRaster is a feature of Oracle Spatial that lets you store, index, query, analyze,

  Oracle, Spatial, Oracle spatial

Lenses for 3D Time-of-Flight (ToF) Image Sensors

IMPORTANT NOTICE FOR TI DESIGN INFORMATION AND RESOURCES Texas Instruments Incorporated (‘TI”) technical, application or other design advice, …

Photography for Beginers - Canadian Nature Photographer

1. ISO Speed - determines the digital sensors sensitivity to light and the amount of "grain" in your pictures. When there is lots of light e.g. sunny day, set your camera ISO speed to 100-200. If you want to shoot inside without using a flash, then set your camera to ISO 400, 800,1600 or higher. The higher the ISO speed the grainier your image ...

  Image, Sensor, Photography, Photography for beginers, Beginers

Selection guide - April 2021 Infrared Detectors

InGaAs linear image sensors 05 10152025 16.7 0.9 0.9 1.7 InGaAs area image sensors 0.9 1.9 0.9 2.6 1 5.5 1 25 0.9 10 2.1 0.9 2.55 0.5 2.55 5.3 1 1 11 3.8 Spectral response range (μm) Product name Features Page Product name Spectral response range (μm) Features Page InSb photoconductive detectors InSb photovoltaic detectors InAs photovoltaic ...

  Image, Sensor, Image sensors

Optics - Opto Engineering

Fig. 7: Common area scan sensors format Fig. 8: Area scan sensors relative sizes Fig. 6: Common line scan sensors formats Fig. 5: Mxx mount mechanical layouts F-mount is a bayonet-style mount originally developed by Nikon for its 35 mm format cameras, and is still found in most of its digital SLR cameras. It is commonly used with bigger sensors ...

  Topics, Size, Sensor