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 review of propensity score: principles, methods and ...

Matching Different matching algorithms have been proposed Some practical guidance for the implementation of propensity score matching (Caliendo, 2005) A.Grotta - R.Bellocco A review of propensity score in Stata

  Score, Matching, Score matching

Analysis of the impact of employment on re-offending ...

Propensity Score Matching. Matching enables us to more confidently estimate the impact of P45 employment post-custody on re-offending by minimising the differences between the offenders who did and did not get P45 employment on other characteristics. A limitation here is that the data used for matching is

  Employment, Score, Matching, Score matching

The PSMATCH Procedure - SAS

Optimal matching selects all control units that match each treated unit by minimizing the total absolute difference in propensity score across all matches. Optimal matching selects all matches simultaneously and without replacement. Three methods for optimal matching are available: fixed ratio matching, variable ratio matching, and full matching.

  Score, Matching, Propensity, Propensity score

Lecture 7: Correspondence Matching

Robert Collins “SSD” or “block matching” (Sum of Squared Differences) 1) The most popular matching score. 2) We used it when deriving Harris corners 3) T&V claim it works better than cross-correlation Camps, PSU

  Lecture, Score, Matching, Correspondence, Lecture 7, Score matching, Correspondence matching

Charting Outcomes in the Match - The Match, National ...

Resident Matching Program® (NRMP®) and the Association of American Medical Colleges® (AAMC®). Match outcome data from the ... A high score is not a guarantee of success, and a low score is not a bar to success. Even in the most competitive specialties a few individuals with high scores are not successful. In the less competitive specialties ...

  Outcome, Score, Matching, Match, Charting, Charting outcomes in the match

The VB-MAPP: Conducting the Assessment and Identifying ...

•Visual perceptual skills and matching-to-sample •Grammatical and syntactical skills •Group and classroom skills •Beginning academic skills The VB-MAPP Milestones: How to Score the Assessment There are 3 developmental levels 0-18 months Typical development 18-30 months Typical development 30-48 months Typical development

  Score, Matching

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

image and sentence matching methods. 1. Introduction Image and sentence matching is one of the fundamen-tal tasks in the field of vision and language [26, 6, 13]. The goal of such a cross-modal matching task is how to accurately measure the visual-semantic similarity between an image and a sentence, and is related to many vision-

  Matching