Center-Based 3D Object Detection and Tracking

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

BASNet: Boundary-Aware Salient Object Detection

[20] and salient object detection [3]. Our work focuses on the second branch and aims at accurately segmenting the pixels of salient objects in an input image. The results have immediate applications in e.g. image segmentation/editing [53, 25, 11, 54] and manipulation [24, 43], visual tracking [32, 52, 55] and user interface optimization [12].

  Tracking, Object, Detection, Object detection

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

JOURNAL OF LATEX CLASS FILES 1 FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking Yifu Zhang , Chunyu Wang , Xinggang Wangy, Wenjun Zeng, Wenyu Liu Abstract—There has been remarkable progress on object detection and re-identification (re-ID) in recent years which are the key components of multi-object tracking.

  Tracking, Object, Detection, Object detection, Object tracking

VEHICLE ACCIDENT DETECTION SYSTEM BY USING GSM …

Tracking of accident is a crucial process in the ... In this project we are using accident detection unit which fitted the vibration sensor in the vehicle. For example, In case of accident, occurs if the car is hit to some other vehicle or an object it create some vibration in that case then the vibration sensor will detect the vibrating ...

  Using, Tracking, Object, Detection

Visual Object Tracking using Adaptive Correlation Filters

Visual Object Tracking using Adaptive Correlation Filters David S. Bolme J. Ross Beveridge Bruce A. Draper Yui Man Lui Computer Science Department Colorado State University Fort Collins, CO 80521, USA bolme@cs.colostate.edu Abstract Althoughnotcommonlyused, correlationfilterscantrack complex objects through rotations, occlusions and other

  Using, Tracking, Object, Object tracking using

OpenCV - Tutorialspoint

OpenCV is a cross-platform library using which we can develop real-time computer vision applications. It mainly focuses on image processing, video capture and analysis including features like face detection and object detection. In this tutorial, we explain how you can use OpenCV in your applications. Audience

  Using, Tutorialspoint, Object, Detection, Opencv, Object detection

Face Mask Detection using Machine Learning and Deep …

object detection and face detection. Fig-3- OpenCV We use the OpenCV library to execute infinite loops using our webcam, which detects faces using cascade classifications. The library has over 2000 optimized and advance algorithms for computer vision based machine learning. These algorithms can be used for face detection and

  Using, Object, Detection, Object detection, Detection using

Object Detection and Tracking using Deep Learning and ...

Object detection is identifying object or locating the instance of interest in-group of suspected frames. Object tracking is identifying trajectory or path; object takes in the concurrent frames. Image obtained from dataset is, collection of frames. Basic block diagram of object detection and tracking is shown in Fig. 1. Data set is

  Using, Tracking, Object, Detection, Object detection, Object tracking, Object detection and tracking using, Object detection and tracking

Object Tracking: A Survey - UCF CRCV

motion, edges, etc., which are commonly used in object tracking. Almost all tracking algorithms require detection of the objects either in the first frame or in every frame. Section 4 summarizes the general strategies for detecting the objects in a scene. The suitability of a particular tracking algorithm depends on object appearances, object

  Tracking, Object, Detection, Object tracking

1 Object Detection in 20 Years: A Survey

object detection forms the basis of many other computer vision tasks, such as instance segmentation [1–4], image captioning [5–7], object tracking [8], etc. From the appli-cation point of view, object detection can be grouped into two …

  Tracking, Object, Detection, Object detection, Object tracking