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

kinyiu@iis.sinica.edu.tw, ihyeh@emc.com.tw, and liao@iis ...

learned can accurately correspond to a specific phys-ical characteristic, and we also present it in a visual way. We also confirmed that if operators that con-form to the physical meaning of an objective, it can be used to integrate implicit knowledge and explicit knowledge, and it will have a multiplier effect.

  Have, Learned

Face2Face: Real-time Face Capture and Reenactment of RGB ...

ever, we use robust and dense global photometric alignment, which we enforce with an efficient data-parallel optimiza-tion strategy on the GPU. Online RGB-D Performance Capture Weise et al. [32] capture facial performances in real-time by fitting a para-metric blendshape model to RGB-D data, but they require a professional, custom capture setup.

  Alignment

arXiv:2112.01406v2 [cs.LG] 8 Dec 2021

we first maintain a candidate set from unlabeled target data with higher free energies and then select samples with sig-nificant MvSM values from candidates. Furthermore, free energy can also serve as a regularization signal in the form of an alignment loss to implicitly diminish domain shift, which is complementary to our active strategy.

  Alignment

Strategic Planning in an Educational Development Centre ...

We begin with a literature review highlighting those areas that have been particularly helpful for building our current understanding of strategic planning and for framing our planning efforts. Included is a brief history of strategic planning in universities and important criticisms and benefits of strategic planning.

  Have

FDA: Fourier Domain Adaptation for Semantic Segmentation

on the source. As a paragon (upper bound), we use a state-of-the-art model with adversarial training [19]. We expect that such a simple, “zero-shot” alignment of low-level statistics would improve the baseline, and hopefully come close to the paragon. However, the method actually outperforms the paragon in semantic segmentation. We do

  Alignment

RAFT: Recurrent All-Pairs Field Transforms for Optical Flow

(SGM) algorithm [19]. Like DCFlow, we also constructed 4D cost volumes over learned features. However, instead of processing the cost volumes using SGM, we use a neural network to estimate flow. Our approach is end-to-end differen-tiable, meaning the feature encoder can be trained with the rest of the network

  Learned

Introduction to Microsoft PowerPoint

Adding or changing clip art 1. Click in the clip art area. The clip art gallery will appear. 2. Type a keyword into the clipart gallery search box and click on Go. 3. Click on the desired clip art that appears in the gallery to insert it into your slide. Inserting additional text boxes 1. On the

  Introduction, Microsoft, Powerpoint, Introduction to microsoft powerpoint