Two-Stream Adaptive Graph Convolutional Networks for ...

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

Convolutional Neural Networks for Visual Recognition

Choy et al., 3D-R2N2: Recurrent Reconstruction Neural Network (2016) Mandlekar and Xu et al., Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations (2020) Xu et al., PointFusion: Deep Sensor Fusion for 3D Bounding Box Estimation (2018) 3D Vision & Robotic Vision Wang et al., 6-PACK: Category-level 6D Pose Tracker with

  Network, Visual, Recognition, Neural network, Neural, Convolutional, Convolutional neural networks for visual recognition

Introduction to Machine Learning Final Exam

(7) [4 pts] To the left of each graph below is a number. Select the choices for which the number is the multiplicity of the eigenvalue zero in the Laplacian matrix of the graph. A: 1 B: 1 C: 2 D: 4 The multiplicity is equal to the number of connected components in the graph. (8) [4 pts] Given the spectral graph clustering optimization problem

  Machine, Learning, Graph, Machine learning

Link Prediction Based on Graph Neural Networks

Graph neural network Figure 1: The SEAL framework. For each target link, SEAL extracts a local enclosing subgraph around it, and uses a GNN to learn general graph structure features for link prediction. Note that the heuristics listed inside the box are just for illustration – the learned features may be completely different from existing ...

  Network, Graph, Neural, Graph neural network, Graph neural

JOURNAL OF LA A Comprehensive Survey on Graph Neural …

Graph neural networks are categorized into four groups: recurrent graph neural networks, convo-lutional graph neural networks, graph autoencoders, and spatial-temporal graph neural networks. Comprehensive review We provide the most compre-hensive overview of modern deep learning techniques for graph data. For each type of graph neural network, we

  Network, Survey, Comprehensive, Graph, Neural, Graph neural network, A comprehensive survey on graph neural, Graph neural

LightGCN: Simplifying and Powering Graph Convolution ...

Graph Neural Network ACM Reference Format: Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval

  Network, Graph, Neural, Graph neural network

Chapter 5 The Expressive Power of Graph Neural Networks

work, the message passing neural network, describing the limitations of its expres-sive power and discussing its efficient implementations. In Section 5.4, we will in-troduce a number of methods that make GNNs more powerful than the message passing neural network. In Section 5.5, we will conclude this chapter by discussing further research ...

  Network, Graph, Neural network, Neural, Graph neural

MixGCF: An Improved Training Method for Graph Neural ...

MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems Tinglin Huang†★, Yuxiao Dong‡, Ming Ding♦, Zhen Yang♦, Wenzheng Feng♦ Xinyu Wang†, Jie Tang♦§ †Zhejiang University, ‡Facebook AI, ♦Tsinghua University tinglin.huang@zju.edu.cn,yuxiaod@fb.com,dm18@mails.tsinghua.edu.cn,zheny2751@gmail.com

  Network, Graph, Neural, Graph neural network, Graph neural