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

Lecture 6 Features and Image Matching

Multiscale Oriented PatcheS descriptor 4 0 p i x e l 8 pixels s Adapted from slide by Matthew Brown. Detections at multiple scales. Basic idea: • Take 16x16 square window around detected interest point (8x8 shown below) • Compute edge orientation (angle of …

  Multiscale

Mathematics and Science - NSF

many situations, especially for multiscale and chaotic problems, fast hardware alone will never be sufficient; methods and theories must be developed that can extract the best possible numerical solutions from whatever computers are available. It is important to remember that no amount of computing power or storage can overcome

  Sciences, Mathematics, Multiscale, Mathematics and science

ME185 - University of California, Berkeley

Introduction This is a set of notes written as part of teaching ME185, an elective senior-year under-graduate course on continuum mechanics in the Department of Mechanical Engineering at

Semantic Segmentation

"A discriminatively trained, multiscale, deformable part model." In Computer Vision and Pattern Recognition, 2008. CVPR. [13] Girshick, Ross, et al. "Deformable part models are convolutional neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

  Multiscale

Bromination of Cinnamic acid

laboratory: with multistep and multiscale synthesis, 5nd edition, Wiley Custom Services, chaper 7, pp 486). Photos of the experiment Figure SM 4.1.1.1.1.1. The cinnamic acid solubilization in CH2Cl2 Figure SM 4.1.1.1.1.2. Reaction apparatus before the Br2 addition

  Multiscale

Chapter 4 Fundamentals of Laser-Material Interaction and ...

Chapter 4 Fundamentals of Laser-Material Interaction and Application to Multiscale Surface Modification Matthew S. Brown and Craig B. Arnold Abstract Lasers provide the ability to accurately deliver large amounts of energy into confined regions of a material in order to achieve a desired response.

  Laser, Multiscale

Multiscale Vision Transformers - arXiv

Multiscale Vision Transformers learn a hierarchy from dense (in space) and simple (in channels) to coarse and complex features. Several resolution-channel scale stages progressively increase the channel capacity of the intermediate latent sequence while reducing its length and thereby spatial resolution.

  Vision, Transformers, Multiscale, Multiscale vision transformers

Improved Multiscale Vision Transformers for Classification ...

The multiscale transformer features naturally integrate with stan-dard feature pyramid networks (FPN). token i, respectively. Note that Rt is optional and only required to support temporal dimension in the video case. In comparison, our decomposed embeddings reduce the number of learned embeddings to O(T+W+H), which can have

  Multiscale

ICCV 2021 Prizes

Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields Jonathan T Barron, Ben Mildenhall (Google Research). Matthew Tancik (UC Berkeley), Peter Hedman (Google Research), Ricardo Martin-Brualla (Google), Pratul Srinivasan (Google Research) Session 5 (A/B)

  Multiscale