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.

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

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

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

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

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

Dynamic Head: Unifying Object Detection Heads With …

Object detection is to answer the question “what ob- ... Instead of image pyramid, feature pyramid [14] was ... Convolution neural networks were known to be limited in learning spatial transformations existed in im-ages [36]. Some works mitigate this problem by either in-

  Feature, Network, Object, Detection, Pyramid, Object detection, Feature pyramid

kinyiu@iis.sinica.edu.tw, ihyeh@emc.com.tw, and liao@iis ...

of neural networks, as shown in Figure4.(b). The above mode of operation can be widely used in different fields, such as the feature alignment of large objects and small objects in feature pyramid networks (FPN) [8], the use of knowledge distillation to integrate large models and small models, and the handling of zero-shot domain transfer and

  Feature, Network, Pyramid, Feature pyramid networks

The Viola/Jones Face Detector - University of British Columbia

A widely used method for real-time object detection. Training is slow, but detection is very fast. ... • A 20 feature classifier achieve 100% detection rate with 10% false positive rate (2% cumulative) ... using image pyramid • Orientation selection • …

  Feature, Object, Detection, Pyramid, Object detection

Faster R-CNN: Towards Real-Time Object Detection with ...

1 Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun Abstract—State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these …

  Network, Object, Detection, Faster, Object detection, Faster r cnn, Object detection networks

Rich Feature Hierarchies for Accurate Object Detection and ...

2. Object detection with R-CNN Our object detection system consists of three modules. The first generates category-independent region proposals. These proposals define the set of candidate detections avail-able to our detector. The second module is a large convo-lutional neural network that extracts a fixed-length feature vector from each ...

  Feature, Object, Detection, Object detection

Faster R-CNN: Towards Real-Time Object Detection with ...

1 Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun Abstract—State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these …

  Network, With, Time, Proposal, Regions, Real, Towards, Object, Detection, Object detection networks, Towards real time object detection, Towards real time object detection with region proposal networks

Abstract arXiv:1411.4038v2 [cs.CV] 8 Mar 2015

Convolutional networks are driving advances in recog-nition. Convnets are not only improving for whole-image classification [19,31,32], but also making progress on lo-cal tasks with structured output. These include advances in bounding box object detection [29,12,17], part and key-point prediction [39,24], and local correspondence [24,9].

  Network, Object, Detection, Object detection

arxiv.org

Created Date: 20170421000942Z