Bottom-Up and Top-Down Attention for Image …
Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering Peter Anderson1 Xiaodong He2 Chris Buehler3 Damien Teney4 Mark Johnson5 Stephen Gould1 Lei Zhang3 1Australian National University 2JD AI Research 3Microsoft Research 4University of Adelaide 5Macquarie University 1firstname.lastname@anu.edu.au, 2xiaodong.he@jd.com, …
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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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RegularFace: Deep Face Recognition via Exclusive ...
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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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