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Image Super-Resolution With Non-Local Sparse Attention
dictionary such as wavelet [11] and curvelet [9] functions. Combining with exemplar-based approaches, sparse repre-sentation developed the dictionary using raw image patches [46] or learned semantic feature patches from the degraded image itself [17, 19] or external datasets[47]. As the deep Convolution Neural Networks (CNNs) for SISR emerges,
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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
Finding Tiny Faces in the Wild With Generative Adversarial ...
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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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openaccess.thecvf.comFrustum 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
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 ...
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