Multi-Similarity Loss With General Pair Weighting for Deep ...

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 [email protected] [email protected] [email protected] 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 [email protected] [email protected]

  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

Edge detection - University of Nevada, Reno

• The Prewitt edge detector-Consider the arrangement of pixels about the pixel (i, j): a0 a7 a6 a1 [i, j] a5 a2 a3 a4-The partial derivativescan be computed by: Mx =(a2 +ca3 +a4) −(a0 +ca7 +a6) My =(a6 +ca5 +a4) −(a0 +ca1 +a2)-The constantc implies the emphasis giventopixels closer to the center of the mask.-Setting c =1, we get the ...

  Detectors, Computed

Histograms of Oriented Gradients for Human Detection

are computed on a dense grid of uniformly spaced cells and they use overlapping local contrast normalizations for im-provedperformance. We make a detailed study of the effects of various implementation choices on detector performance, taking “pedestriandetection” (the detection of mostly visible people in more or less upright poses) as a ...

  Detectors, Computed

SURF: Speeded Up Robust Features

3 Fast-Hessian Detector We base our detector on the Hessian matrix because of its good performance in computation time and accuracy. However, rather than using a different measure for selecting the location and the scale (as was done in the Hessian-Laplace detector [11]), we rely on the determinant of the Hessian for both. Given a point

  Detectors, Surf

The Viola/Jones Face Detector

The Viola/Jones Face Detector (2001) (Most slides from Paul Viola) A widely used method for real-time object detection. Training is slow, but detection is very fast.

  Detectors

Rectified Linear Units Improve Restricted Boltzmann Machines

each feature detector to be 1 with probability p(hj = 1) = 1 1+exp(−bj − P i∈vis viwij) (2) where bj is the bias of j and vi is the binary state of pixel i. Once binary states have been chosen for the hidden units we produce a “reconstruction” of the training image by setting the state of each pixel to be 1 with probability p(vi = 1 ...

  Detectors

PATIENT RADIATION DOSES IN DIAGNOSTIC RADIOLOGY

2 organization of the presentations • part 2: typical radiation dose values, risks & dealing with public – n.e.x.t. surveys – reference values – fetal dose calculation guides – review of some biological risks – dealing with the public - with references patient radiation doses in diagnostic

  Radiation

Tutorial: Checksum and CRC Data Integrity Techniques for ...

3 Checksums and CRCs Protect Data Integrity • Compute check sequence when data is transmitted or stored – Data Word: the data you want to protect (can be any size; often Mbytes) – Check Sequence: the result of the CRC or checksum calculation – Code Word = Data Word with Check Sequence Appended • To check data integrity: – Retrieve or receive Code Word