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

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

You Only Look Once: Unified, Real-Time Object Detection

ized sensors, enable assistive devices to convey real-time scene information to human users, and unlock the potential for general purpose, responsive robotic systems. Current detection systems repurpose classifiers to per-form detection. To detect an object, these systems take a classifier for that object and evaluate it at various locations

  Time, Object, Detection, Time object detection

You Only Look Once: Unified, Real-Time Object Detection

ized sensors, enable assistive devices to convey real-time scene information to human users, and unlock the potential for general purpose, responsive robotic systems. Current detection systems repurpose classifiers to per-form detection. To detect an object, these systems take a classifier for that object and evaluate it at various locations

  Time, Object, Detection, Time object detection

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

effective running time for proposals is just 10 milliseconds. Using the expensive very deep models of [19], our detection method still has a frame rate of 5fps (including all steps) on a GPU, and thus is a practical object detection system in terms of both speed and accuracy (73.2% mAP on PASCAL VOC 2007 and 70.4% mAP on 2012).

  Time, Object, Detection, Object detection, Time object detection

JOURNAL OF LA FairMOT: On the Fairness of Detection and …

addition, feature maps in object detection are usually down-sampled by 8=16=32 times to balance accuracy and speed. This is acceptable for object detection but it is too coarse for learning re-ID features because features extracted at coarse anchors may not be aligned with object centers. 1.2 Unfairness Caused by Features

  Object, Detection, Object detection

1 Object Detection in 20 Years: A Survey - arXiv

Like other object detection algorithms in its time [29–31], the Haar wavelet is used in VJ detector as the feature representation of an image. The integral image makes the computational complexity of each window in VJ detector independent of its window size.

  Time, Object, Detection, Object detection

End-to-End Object Detection with Transformers arXiv:2005 ...

2.3 Object detection Most modern object detection methods make predictions relative to some ini-tial guesses. Two-stage detectors [37,5] predict boxes w.r.t. proposals, whereas single-stage methods make predictions w.r.t. anchors [23] or a grid of possible object centers [53,46]. Recent work [52] demonstrate that the nal performance

  Object, Detection, Object detection

Focal Loss for Dense Object Detection

object categories and had top results on PASCAL [7] for many years. While the sliding-window approach was the leading detection paradigm in classic computer vision, with the resurgence of deep learning [18], two-stage detectors, described next, quickly came to dominate object detection. Two-stage Detectors: The dominant paradigm in modern

  Object, Detection, Object detection