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

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

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

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

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

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-

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

U VC- G 4 - B ullet

B uilt-in I R L ED illumina tion a nd I R cut lte r Vide o comp r e s s ion H . 264 R e s olution 2 68 x 15 ( :9) Ma x imum 2fr a me r a te 4 FP S I ma ge s e tting s B r ih t nes ,c oap ud 5 0/6 H z P r oce s s or ®s p e cs Qua d cor e Ar m Cor te x ®-A53 ba …

  Into, Illumina, Illumina tion

Monte Carlo Integration

dimensional integration problems, such as those encountered when computing global illumina-tion. 155 A.2.2 Multidimensional Integration Monte Carlo integration can be generalized to use random variables drawn from arbitrary PDFs and to compute multidimensional integrals, such as ...

  Into, Integration, Oracl, Monte, Illumina, Illumina tion, Monte carlo integration

limma Linear Models for Microarray and RNA-Seq Data User’s ...

Functions for reading and pre-processing expression data from Illumina BeadChips were intro-duced in limma 3.0.0. See the case study in Section 17.3 for an example of these. Limma can also be used in conjunction with the vst or beadarray packages for pre-processing Illumina data.

  Illumina

An Efficient Representation for Irradiance Environment Maps

We consider the rendering of diffuse objects under distant illumina-tion, as specified by an environment map. Using an analytic expres-sion for the irradiance in terms of spherical harmonic coefficients of the lighting, we show that one needs to compute and use only 9 coefficients, corresponding to the lowest-frequency modes of the

  Into, Illumina, Illumina tion

Learning to See in the Dark - CVF Open Access

extreme low-light imaging with severely limited illumina-tion (e.g., moonlight) and short exposure (ideally at video rate). In this regime, the traditional camera processing pipeline breaks down and the image has to be reconstructed from the raw sensor data. Figure 1 illustrates our setting. The environment is ex-

  Into, Illumina, Illumina tion

How GPUs Work - NVIDIA

ing equation (B-T. Phong, “Illumina-tion for Computer-Generated Images,” Comm. ACM, June 1975, pp. 311-317), a phenomenological appearance model that approximates the look of plastic. These materials combine a dull diffuse base with a shiny specular high-How GPUs Work David Luebke, NVIDIA Research Greg Humphreys, University of Virginia

  Into, Illumina, Illumina tion

Illumina Sequencing Technology

Illumina Sequencing Technology Author: Illumina Subject: Illumina sequencing technology leverages clonal array formation and proprietary reversible terminator technology for rapid and accurate large-scale sequencing. The innovative and flexible sequencing system enables a broad array of applications in genomics\ , transcriptomics, and epigenomics.

  Technology, Sequencing, Illumina, Illumina sequencing technology

N United States Court of Appeals for the Federal Circuit

Dec 14, 2021 · tion based on the theory that the record evidence of use in commerce pointed not to Sunbio, but to SmartNutri in-stead. As explained below, we disagree with Sunbio on both points and affirm. We review the Board’s legal conclusions de novo and its factual findings for substantial evidence. In re Jobdiva, Inc., 843 F.3d 936, 940 (Fed. Cir. 2016).

  Into