Large Scale Visual Recognition Challenge (ILSVRC) 2017
Large Scale Visual Recognition Challenge (ILSVRC) 2017 Eunbyung Park UNC Chapel Hill Overview Wei Liu UNC Chapel Hill Olga Russakovsky CMU/Princeton
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ImageNet: A Large-Scale Hierarchical Image …
www.image-net.orgImageNet: A Large-Scale Hierarchical Image Database Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei Dept. of Computer …
ImageNet: A Large-Scale Hierarchical Image Database
www.image-net.orgof images. We believe that a large-scale ontology of images is a critical resource for developing advanced, large-scale content-based image search and image understanding algo-rithms, as well as for providing critical training and bench-marking data for such algorithms. ImageNet uses the hierarchical structure of WordNet [9].
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openaccess.thecvf.comSqueeze-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-
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arxiv.org1 Squeeze-and-Excitation Networks Jie Hu [000000025150 1003] Li Shen 2283 4976] Samuel Albanie 0001 9736 5134] Gang Sun [00000001 6913 6799] Enhua Wu 0002 2174 1428] Abstract—The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within …
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resources.sei.cmu.edumodels such as SENets, that’s squeeze-and-excitation networks. Carson: So if I can interrupt for a second, a very high-level view of this is that, this is just an image recognition challenge, which is what deep learning kind of came to fruition in proving to
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