ClassSR: A General Framework to Accelerate Super ...

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

A Unified Architecture for Instance and Semantic Segmentation

He, K., Hariharan, B., & Belongie, S. Feature pyramid networks for object detection.CVPR 2017. Feature Pyramid Network (FPN) [3] 3 256 512 1024 2048 256 256 256 256. FPN Architecture 1 4 1 8 1 16 1 32 image 1 2x up 1x1 conv + high resolution low resolution strong features strong features [1] He, K., Zhang, X., Ren, S., & Sun, J. Deep residual ...

  Feature, Pyramid, Feature pyramid

D DETR: DEFORMABLE TRANSFORMERS FOR -E OBJECT ... - …

deformable attention module can naturally aggregate multi-scale feature maps via attention mecha-nism, without the help of these feature pyramid networks. 3 REVISITING TRANSFORMERS AND DETR Multi-Head Attention in Transformers. Transformers (Vaswani et al., 2017) are of a network architecture based on attention mechanisms for machine translation.

  Feature, Transformers, Pyramid, Dret, Deformable, Feature pyramid, Deformable transformers for

Zhi Tian Chunhua Shen Hao Chen Tong He The ... - arXiv

than 180K anchor boxes in feature pyramid networks (FPN) [14] for an image with its shorter side being 800). Most of these anchor boxes are labelled as negative samples dur-ing training. The excessive number of negative samples ag-gravates the imbalance between positive and negative sam-ples in training. 4) Anchor boxes also involve complicated

  Feature, Pyramid, Feature pyramid

Mirrors - Grote Industries

Distinctive pyramid-style back interchanges functionally and aesthetically with competitive units • Shock-resistant mirror glass mounting with a rugged outer gasket • Heated mirror features 5' jacketed power cable • 5/16"-18 mounting stud 16273 Stainless Steel 16142 Black HEATED MIRROR: 16153 Stainless Steel Material: Stainless Steel, Steel

  Industreis, Mirrors, Pyramid, Grote, Grote industries

Histograms of Oriented Gradients for Human Detection

is run on the output pyramid to detect object instances, but this paper concentrates on the feature extraction process. edge directions, even without precise knowledge of the cor-responding gradient or edge positions. In practice this is im-plemented by dividing the image window into small spatial

  Feature, Pyramid

Image Alignment and Stitching: A Tutorial

Dec 10, 2006 · al. 1998, McLauchlan and Jaenicke 2002, Brown and Lowe 2003). Feature-based approaches have the advantage of being more robust against scene movement and are potentially faster, if imple-mented the right way. ... Note that if we work with images in a pyramid, we need to halve the Svalue after each decimation step rather than recomputing it from ...

  Feature, Image, Alignment, Pyramid, Stitching, Image alignment and stitching

PyCharm - Tutorialspoint

PyCharm i About the Tutorial PyCharm is the most popular IDE for Python, and includes great features such as excellent code completion and inspection …

  Tutorialspoint