Multi-Modality Cross Attention Network for Image and ...

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

Research, Research Gap and the Research Problem

re lationships are ac tually the orist’s gue sse s about the re ality. Suc h gue sse s are c alle d as ‘Re se arc h Hypothe sis’ or ‘Propositions’. Finally, the re se arc he r may te st the re se arc h hypothe sis and c onc lude the validity of the the ory. Thus, this is an e …

  Rela tionship, Lationships

2 Related Work

diagnose anomalies by identifying deviations from these re-lationships. Graph-based methods provide a way to model the re-lationships between sensors by representing the inter-dependencies with edges. Such methods include probabilis-tic graphical models, which encode joint probability distri-butions, as described in (Bach and Jordan 2004; Tank ...

  Rela tionship, Lationships

Analytical Procedures - AICPA

consist of evaluations of financial information made by a study of plausible re-lationships among both financial and nonfinancial data. Analytical procedures range from simple comparisons to the use of complex models involving many relationships and elements of data. A basic premise underlying the application

  Procedures, Relationship, Analytical, Analytical procedures, Rela tionship, Lationships

THE RETAIL DISTRIBUTION CHANNEL

goods in anticipation of seasonal sales peaks. Depending on how close their re-lationships, channel members may also work together to purchase goods or services in greater quantity at discounts, passing the savings on to customers. Even for consumers, the distribution chain is handy—beyond handy, in fact! It has become a necessity in our society.

  Distribution, Rela tionship, Lationships

Friendship Quality and Social Development

influence the more positive the re-lationships with those friends are. That is, having high-quality friend-ships with delinquent friends is as-sumed to increase the influence of those friends, thereby increasing the degree to which adolescents be-come like their friends over time. Many other theories include the hypothesis that friends’ influence

  Rela tionship, Lationships

Cracks and Crack Control in Concrete Structures

analysis furnished the following re-lationships for the mean direct tensile strength, f tm , related to the 28-day compressive cylinder strength f,' of con-crete: fcm = 2.1 (fc)z/3 (psi) fi m = 0.34 (ff)2"(N/mm2) The statistical analysis indicated that the coefficient in this equation can be modified to 1.4 (0.22) and 2.7 (0.45) to

  Control, Structure, Concrete, Crack, Rela tionship, Crack control in concrete structures, Lationships