Focal Loss for Dense Object Detection

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

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

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

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

PIXOR: Real-Time 3D Object Detection From Point Clouds

well as camera view) to do 3D object detection. However, hand-crafted features are computed as the encoding of the rasterizedimages. Ourproposeddetector,however,usesthe bird’s eye view representation alone for real-time 3D object detection in the context of autonomous driving, where we assume that all objects lie on the same ground. 3. PIXOR ...

  Time, Object, Detection, 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

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