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

Proximal Gradient Descent - Carnegie Mellon University

Often called theiterative soft-thresholding algorithm (ISTA).1 Very simple algorithm Example of proximal gradient (ISTA) vs. subgradient method convergence curves 0 200 400 600 800 1000 0.02 0.05 0.10 0.20 0.50 k f-fstar Subgradient method Proximal gradient 1Beck and Teboulle (2008), \A fast iterative shrinkage-thresholding algorithm for linear ...

  Descent, Proximal, Derating, Thresholding, Proximal gradient descent

Image Filtering - Auckland

Thresholding and image equalisation are examples of nonlinear operations, as is the median filter. 2 Median Filtering Median filtering is a nonlinear method used to remove noise from images. It is widely used as it is very effective at removing noise while preserving edges. It is particularly effective at removing ‘salt and pepper’ type noise.

  Thresholding

Proximity sensing module - STMicroelectronics

– Window and thresholding functions for ranging Applications Laser Assisted Auto Focus Smartphones/portable touchscreen devices Tablet/laptop/gaming devices Domestic appliances/industrial devices Description The VL6180 is the latest product based on ST’s patented FlightSense™ technology. This is a

  Thresholding

Convolutional Neural Networks (CNNs / ConvNets)

thresholding at zero. This leaves the size of the volume unchanged ([32x32x12]). POOL layer will per form a downsampling operation along the spatial dimensions (width, height), resulting in volume such as [16x16x12]. FC (i.e. fully-connected) la yer will compute the class scores, resulting in volume of size

  Network, Neural network, Neural, Thresholding

ImageJ Batch Processing

Standard features include particle analysis, thresholding and histogram statistics. The analysis capabilities of ImageJ can be significantly expanded by using third party plugins. Plugins must conform to the ImageJ plugin framework. When placed in the ImageJ plugins directory they are automatically identified by ImageJ and become available

  Processing, Batch, Imagej, Thresholding, Imagej batch processing

MATLAB for image processing --- A guide to basic MATLAB ...

image in the range o 0-255. 4) Create a binary (black and white) image by thresholding the original grayscale image using a prescribed threshold T so that an original gray value V is change to 0 if V is below T, and is changed to 255 if V is above T. Hint: The last step can be easily accomplished in MATLAB by Bimg = (Img > T)*255;

  Thresholding

OpenCV - Tutorialspoint

OpenCV 1 About the Tutorial OpenCV is a cross-platform library using which we can develop real-time computer vision applications.It mainly focuses on image processing, video capture and analysis including

  Tutorialspoint, Opencv

HMMER User’s Guide - Eddy Lab

Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 hmmlogo - given an HMM, produce data required to build an ...