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

CartoonGAN: Generative Adversarial Networks for Photo ...

is to use Generative Adversarial Networks (GANs) [9, 34], which produce state-of-the-art results in many applications suchastexttoimagetranslation[24],imageinpainting[37], image super-resolution [19], etc. The key idea of a GAN model is to train two networks (i.e., a generator and a dis-criminator) iteratively, whereby the adversarial loss pro-

  Network, Adversarial, Generative, Generative adversarial networks

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

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

(Text with EEA relevance) - European Commission

Digital services can support achieving Sustainable Development Goals by contributing to economic, social and environmental sustainability. ... adopted a resolution under the non-legislative procedure on the ‘Digital Services Act and ... the proposal sets a higher standard of transparency and accountability on how the

  Higher, European commission, European, Commission, Achieving, Resolution

ROADMAP FOR CARBON RNC2050) - UNFCCC

RESOLUTION OF THE COUNCIL OF MINISTERS P. 4 P. 6 P. 6 P. 9 P. 10 P. 12 P. 14 P. 16 P. 25 P. 30 P. 35 P. 39 P. 43 P. 43 P. 44 ... Achieving carbon neutrality implies changing from a linear, fossil fuel-based economic model to a circular, carbon-neutral model. ... temperature increase in more vulnerable areas of the planet could be even higher ...

  Higher, Achieving, Resolution

2022 chemical industry outlook

its contribution to achieving the COP21 climate resolution.12 As the global energy industry continues to shift from fossil fuels to renewables and the “Green Deal” gains steam, this momentum is expected to continue into 2022. Specifically, industry players may show a heightened focus on new and innovative technologies

  Achieving, Resolution

Caseload and Workload Management - Child Welfare

show that departing caseworkers did not have higher-than-average caseloads. It may be that the workers in the latter studies perceive their caseloads as being too high or causing additional stress, which contributes to poor organizational commitment and decisions to leave (e.g., Bowling, Alarcon, Bragg, & Hartman, 2015; Kim & Kao, 2014).

  Higher, Management, Workload, Child, Child welfare, Welfare, Caseload, Caseload and workload management

The effectiveness of Time Management Strategies ...

example, high achieving students were found to exhibit more self-regulated learning skills (Zimmerman & Martinez-Pons, 1990), and with time management in particular (Eilam & Aharon, 2003). The construct of self-efficacy has been studied to determine issues related to how

  Achieving

Restorative Justice in U.S. Schools - ed

ii Restorative Justice in U.S. Schools: An Updated Research Review Impact on school climate and safety 30 Impact on academic outcomes 31 Access to restorative justice 32 Limitations of the Literature Review 33 Limited sample 33 Limited causal research 33 Small sample sizes 34 Implementation challenges 34 Conclusion 35 References 37 Appendix: Glossary of Restorative …