Abstract

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007 Technical Report IDSIA-07-07 A Collection of Definitions of Intelligence Shane Legg IDSIA, Galleria …

  Intelligence, Collection

Deep Residual Learning for Image Recognition - …

Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com

  Image, Learning, Residual, Recognition, Residual learning for image recognition

arXiv:1301.3781v3 [cs.CL] 7 Sep 2013

For all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …

@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016

where 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …

A Tutorial on UAVs for Wireless Networks: …

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …

  Network, Communication, Wireless, Wireless communications, Wireless networks

Adversarial Generative Nets: Neural Network …

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University

  Network, Attacks, Nets, Adversarial generative nets, Adversarial, Generative, Neural network, Neural, Neural network attacks

Massive Exploration of Neural Machine Translation ...

Massive Exploration of Neural Machine Translation Architectures Denny Britzy, Anna Goldie, Minh-Thang Luong, Quoc Le fdennybritz,agoldie,thangluong,qvlg@google.com Google Brain

  Architecture, Machine, Exploration, Translation, Neural, Exploration of neural machine translation, Exploration of neural machine translation architectures

Mastering Chess and Shogi by Self-Play with a …

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver, 1Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, 1Matthew Lai, Arthur Guez, Marc Lanctot,1

Going deeper with convolutions - arXiv

Going deeper with convolutions Christian Szegedy Google Inc. Wei Liu University of North Carolina, Chapel Hill Yangqing Jia Google Inc. Pierre Sermanet

  With, Going, Going deeper with convolutions, Deeper, Convolutions

Andrew G. Howard Menglong Zhu Bo Chen Dmitry ...

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto Hartwig Adam

  Applications

Abstract arXiv:2103.02603v2 [cs.CV] 9 May 2021

knowledge base. This would define a smart object detection system, and ours is an effort towards achieving this goal. The key contributions of our work are: •We introduce a novel problem setting, Open World Object Detection, which models the real-world more closely. •We develop a novel methodology, called ORE, based on

  Real, Towards, Object, Detection, Object detection

Florence: A New Foundation Model for Computer Vision

cation) to fine-grained (e.g. object detection), 2) Time: from static (e.g. images) to dynamic (e.g. videos), and 3) Modal-ity: from RGB only to multiple senses (e.g. captioning and depth). Due to the diversity nature of visual understanding, we …

  Time, Object, Detection, Object detection

EfficientDet: Scalable and Efficient Object Detection

towards more accurate object detection; meanwhile, state-of-the-art object detectors also become increasingly more expensive. For example, the latest AmoebaNet-based NAS-FPN detector [42] requires 167M parameters and 3045B FLOPs (30x more than RetinaNet [21]) to achieve state-of-the-art accuracy. The large model sizes and expensive com-

  Towards, Object, Detection, Object detection

Towards End-to-End License Plate Detection and …

Towards End-to-EndLicense Plate Detection and Recognition: A Large Dataset and Baseline Zhenbo Xu1,2[0000−0002−8948−1589], Wei Yang 1( )[0000−0003−0332−2649], Ajin Meng1,2, Nanxue Lu1,2, Huan Huang2, Changchun Ying2, and Liusheng Huang1 1 School of Computer Science and Technology, University of Science and Technology of China, Hefei, China

  Large, Plate, Towards, Recognition, Detection, A regal, Plate detection and, Plate detection and recognition

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

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren Kaiming He Ross Girshick Jian Sun Microsoft Research fv-shren, kahe, rbg, jiansung@microsoft.com Abstract State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [7] and Fast R ...

  Time, Real, Towards, Object, Detection, Object detection, Towards real 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, Real, 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, Real, Object, Detection, Time object detection

Time of Flight Principles, Challenges, and Performance …

Time of Flight Basics Active illumination system: • Laser emits light (photons) towards a target • Light (partially) reflected from the target • Sensor area determines when light (photons) arrives • ToF is translated into distance Distance Value = Photon travel time/2 x by speed of light

  Performance, Principles, Challenges, Time, Flight, Towards, Performance and, Flight principles