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

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-

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

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.

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

ImageNet: A Large-Scale Hierarchical Image Database

of images. We believe that a large-scale ontology of images is a critical resource for developing advanced, large-scale content-based image search and image understanding algo-rithms, as well as for providing critical training and bench-marking data for such algorithms. ImageNet uses the hierarchical structure of WordNet [9].

  Database, Large, Scale, Image, Hierarchical, Imagenet, A regal, A large scale hierarchical image database

Tech report (v5) - arXiv

by showing substantially higher image classification accu-racy on the ImageNet Large Scale Visual Recognition Chal-lenge (ILSVRC) [9,10]. Their success resulted from train-ing a large CNN on 1.2 million labeled images, together with a few twists on LeCun’s CNN (e.g., max(x;0) rectify-ing non-linearities and “dropout” regularization).

  Large, Scale, Image, A regal, Large scale

Methods for 3D Reconstruction from Multiple Images

Multi-scale Approach • Optimizing only a narrow band • Progressive refinement ªAbout 10 to 30 minutes (and no exact silhouettes) input result [Hornung 06] intermediate scales [Hornung 06] [Hornung 06] A. Hornung and L. Kobbelt. Hierarchical volumetric multi-view stereo reconstruction of manifold surfaces based on dual graph embedding. CVPR ...

  Scale, Hierarchical

Deep Layer Aggregation - arXiv

and ResNeXt [41] for large-scale image classification, fine-grained recognition, semantic segmentation, and boundary detection. Our results show improvements in performance, parameter count, and memory usage over baseline ResNet, ResNeXT, and DenseNet architectures. DLA achieve state-of-the-art results among compact models for classification.

  Large, Scale, Image, Scale image

Multi-scale Residual Network for Image Super-Resolution

Multi-scale Residual Network for Image Super-Resolution Juncheng Li1[0000−0001−7314−6754], Faming Fang1[0000−0003−4511−4813], Kangfu Mei2[0000−0001−8949−9597], and Guixu Zhang1[0000−0003−4720−6607] 1 Shanghai Key Laboratory of Multidimensional Information Processing, and Department of Computer Science & Technology, East China Normal University,

  Scale, Image

Hydrologic Unit Codes: HUC 4, HUC 8, and HUC 12 ... - US EPA

the subregion level, delineating large river basins (shown in yellow in the image). HUC 8 maps the subbasin level, analogous to medium-sized river basins (about 2200 nationwide, pictured in red in the image); and HUC 12 is a more local sub-watershed level that captures tributary

  Large, Image

Siamese Neural Networks for One-shot Image Recognition

a method called Hierarchical Bayesian Program Learning (HBPL) (2013). In a series of several papers, the authors modeled the process of drawing characters generatively to decompose the image into small pieces (Lake et al.,2011; 2012). The goal of HBPL is to determine a structural ex-planation for the observed pixels. However, inference un-

  Image, Hierarchical