Objects as Points

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

Focal Loss for Dense Object Detection

Focal Loss for Dense Object Detection Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He Piotr Doll´ar Facebook AI Research (FAIR) well-classi ed examples CE(p t) = log(p t) FL(p t) = (1 p t) log(p t) Figure 1. We propose a novel loss we term the Focal Loss that adds a factor (1 Enabled by the focal loss, our simple one-stagep

  Loss, Object, Detection, Falco, Dense, Focal loss for dense object detection, Focal loss

CornerNet: Detecting Objects as Paired Keypoints

are the first to formulate the task of object detection as a task of detecting and grouping corners simultaneously. Another novelty of ours is the corner pooling layers that help better localize the corners. We also significantly modify the hourglass architecture and add our novel variant of focal loss [23] to help better train the network. 3 ...

  Loss, Detecting, Object, Detection, Falco, Object detection, Rencontre, Focal loss, Detecting objects as

Focal Loss for Dense Object Detection - CVF Open Access

3. Focal Loss The Focal Loss is designed to address the one-stage ob-ject detection scenario in which there is an extreme im-balancebetween foregroundand backgroundclasses during training (e.g., 1:1000). We introduce the focal loss starting from the cross entropy (CE) loss for binary classification1: CE(p,y)= (−log(p) if y =1 −log(1−p ...

  Loss, Object, Detection, Falco, Ject, Dense, Focal loss for dense object detection, Focal loss, Ob ject detection

Generalized Intersection over Union: A Metric and A Loss ...

lem, the authors later introduce focal loss [13], which is orthogonal to the main focus of our paper. Most popular object detectors [20, 21, 3, 12, 13, 16] uti-lize some combination of the bounding box representations and losses mentioned above. These considerable efforts have yielded significant improvement in object detection.

  Loss, Object, Detection, Falco, Object detection, Focal loss