Deep High-Resolution Representation Learning for Human …
video pose estimation and tracking [48, 70], etc. The recent developments show that deep convolutional neural networks have achieved the state-of-the-art perfor-mance. Most existing methods pass the input through a network, typically consisting of high-to-low resolution sub-networks that are connected in series, and then raise the resolution ...
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What Have We Learned From Deep Representations for …
openaccess.thecvf.comwhat 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
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Squeeze-and-Excitation Networks - openaccess.thecvf.com
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Protecting World Leaders Against Deep Fakes
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Auto-DeepLab: Hierarchical Neural Architecture Search for ...
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PointNet: Deep Learning on Point Sets ... - CVF Open Access
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Frustum PointNets for 3D Object Detection From RGB-D Data
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Class-Balanced Loss Based on Effective Number of Samples
openaccess.thecvf.comand 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
ESRGAN: Enhanced Super-Resolution Generative Adversarial ...
openaccess.thecvf.comESRGAN: 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, …
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MATHEMATICS KINDERGARTEN TO GRADE 9 - Alberta
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