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

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

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

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.

Deconvolutional Networks - matthewzeiler

Our proposed model is similar in spirit to the Convo-lutional Networks of LeCun et al. [13], but quite different in operation. Convolutional networks are a bottom-up (a) (b) Figure 1. (a): “Tokens” from Fig. 2-4 of Vision by D. Marr [18]. These idealized local groupings are proposed as an intermediate

  Network, Convolutional, Convolutional networks, Convos, Convolu tional networks, Lutional

Densely Connected Convolutional Networks - arXiv

embrace this observation and introduce the Dense Convo-lutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with Llayers have L connections—one between each layer and its subsequent layer—our network has L(L+1) 2 direct connections. For

  Network, Convolutional, Convolutional networks, Densenet, Convos, Lutional

Densely Connected Convolutional Networks

embrace this observation and introduce the Dense Convo-lutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections—one between each layer and its subsequent layer—our network has L(L+1) 2 direct connections. For

  Network, Convolutional, Convolutional networks, Convos, Lutional

Learning Convolutional Neural Networks for Graphs

Finally, feature learning components such as convo-lutional and dense layers are combined with the normalized neighborhood graphs as the CNN’s receptive fields. Figure2illustrates the PATCHY-SAN architecture which ... quence Neural Networks modify GNNs to use gated recur-rent units and to output sequences (Li et al.,2015).

  Network, Neural network, Neural, Convos, Lutional

Spatial Pyramid Pooling in Deep Convolutional Networks …

The convo-lutional layers operate in a sliding-window manner and output feature maps which represent the spatial arrangement of the activations (Figure2). In fact, con- ... networks [3] cannot; 2) SPP uses multi-level spatial bins, while the sliding window pooling uses only a single window size. Multi-level pooling has been

  Network, Pyramid, Spatial, Convolutional, Convolutional networks, Pooling, Convos, Lutional, Spatial pyramid pooling

Optimizing FPGA-based Accelerator Design for Deep ...

based on traditional arti cial neural networks. The purpose of this classi er is to decide the likelihood of categories that the input (e.g. image) might belong to. ... will form the set of input feature maps for the next convo-lutional layer. The pseudo code of a convolutional layer can be written as that in Code 1. Figure 1: Graph of a ...

  Network, Convos, Lutional

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

tion networks, we propose a training scheme that alternates between fine-tuning for the region proposal task and then fine-tuning for object detection, while keeping the proposals fixed. This scheme converges quickly and produces a unified network with convo-lutional features that are shared between both tasks.1

  Network, Faster, Convos, Faster r cnn, Lutional