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.

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

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

Learning Deep Features for Discriminative Localization

Weakly-supervised object localization: There have been a number of recent works exploring weakly-supervised object localization using CNNs [1, 16, 2, 15]. Bergamoetal [1]propose atechniqueforself-taughtobject localization involving masking out image regions to iden-tify the regions causing the maximal activations in order to localize objects.

  Object, Supervised, Localization, Supervised object localization

Abstract arXiv:2102.08318v2 [cs.CV] 6 Apr 2021

supervised models, we find that this is not actually the case. We propose a novel approach, called Instance Localization (InsLoc), which sacrifices ImageNet classification accuracy, but enjoys bet-ter generalization ability for object detection. supervised representations which improve upon image clas-

  Object, Supervised, Localization

Video Understanding - Stanford Artificial Intelligence ...

May 07, 2021 · object appearance and pose, viewpoint, background, illumination, etc. ... No human localization Gunnar et al., Hollywood in Homes: Crowdsourcing Data Collection for Activity Understanding, ECCV 2016 ... Allows for self-supervised learning of interesting things Actions Depth Tracking Objects.

  Object, Supervised, Localization

ChestX-ray8: Hospital-Scale Chest X-Ray Database and ...

weakly-supervised multi-label image classification and dis-ease localization framework to address this difficulty. 3, So far, all image captioning and VQA techniques in com-puter vision strongly depend on the ImageNet pre-trained deep CNN models which already perform very well in a large number of object classes and serves a good baseline

  Object, Supervised, Localization

Generalized Focal Loss V2: Learning Reliable Localization ...

Dense object detector [28, 23, 42, 33, 18, 27] which di-rectly predicts pixel-level object categories and bounding boxes over feature maps, becomes increasingly popular due to its elegant and effective framework. One of the cru-cial techniques underlying this framework is Localization Quality Estimation (LQE). With the help of better LQE,

  Object, Localization

Tech report (v5) - arXiv

precise localization within the sliding-window paradigm an open technical challenge. Instead, we solve the CNN localization problem by oper-ating within the “recognition using regions” paradigm [21], which has been successful for both object detection [39] and …

  Object, Localization

Bilinear CNN Models for Fine-grained Visual Recognition

sance factors is to first localize various parts of the object and model the appearance conditioned on their detected locations. The parts are often defined manually and the part detectors are trained in a supervised manner. Recently variants of such models based on convolutional neural net-works (CNNs) [2, 38] have been shown to significantly

  Fine, Object, Supervised

Hotels Booking Management System - mu.edu.sa

Game localization English, Arabic Supported platforms Php,codeginator 3.2 Procedures 3.3 Reports 3.1 List of functionalities that were checked Functionality Result Check internet-connection on the device passed Check that website size corresponds to the approved marketing failed Check that website is responsive, on all screens passed

  Localization