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公开(公告)号：US10572777B2
公开(公告)日：2020-02-25
申请号：US15436199
申请日：2017-02-17
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Feng Zhou , Manmohan Chandrakar
Abstract: A system and method are provided. The system includes a processor. The processor is configured to generate a response map for an image, using a four stage convolutional structure. The processor is further configured to generate a plurality of landmark points for the image based on the response map, using a shape basis neural network. The processor is additionally configured to generate an optimal shape for the image based on the plurality of landmark points for the image and the response map, using a point deformation neural network. A recognition system configured to identify the image based on the generated optimal shape to generate a recognition result of the image. The processor is also configured to operate a hardware-based machine based on the recognition result.
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2.发明申请公开(公告)号：US20160307072A1
公开(公告)日：2016-10-20
申请号：US15095260
申请日：2016-04-11
Applicant: NEC Laboratories America, Inc.
Inventor： Feng Zhou , Yuanqing Lin
CPC classification number: G06K9/66 , G06K9/469 , G06K9/6267 , G06K9/627 , G06N3/0454 , G06N3/0472 , G06N3/084 , G06T7/0004 , G06T2207/30128
Abstract: Systems and methods are disclosed for deep learning and classifying images of objects by receiving images of objects for training or classification of the objects; producing fine-grained labels of the objects; providing object images to a multi-class convolutional neural network (CNN) having a softmax layer and a final fully connected layer to explicitly model bipartite-graph labels (BGLs); and optimizing the CNN with global back-propagation.
Abstract translation: 公开了用于深入学习和分类对象的图像的系统和方法，通过接收用于对象的训练或分类的对象的图像; 生产物品的细粒标签; 向具有softmax层和最终完全连接的层的多级卷积神经网络（CNN）提供对象图像以明确地模拟二分图标签（BGL）; 并利用全局反向传播优化CNN。
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公开(公告)号：US10074041B2
公开(公告)日：2018-09-11
申请号：US15095260
申请日：2016-04-11
Applicant: NEC Laboratories America, Inc.
Inventor： Feng Zhou , Yuanqing Lin
CPC classification number: G06K9/66 , G06K9/469 , G06K9/6267 , G06K9/627 , G06N3/0454 , G06N3/084 , G06T7/0004 , G06T2207/30128
Abstract: Systems and methods are disclosed for deep learning and classifying images of objects by receiving images of objects for training or classification of the objects; producing fine-grained labels of the objects; providing object images to a multi-class convolutional neural network (CNN) having a softmax layer and a final fully connected layer to explicitly model bipartite-graph labels (BGLs); and optimizing the CNN with global back-propagation.
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公开(公告)号：US20170262736A1
公开(公告)日：2017-09-14
申请号：US15436199
申请日：2017-02-17
Applicant: NEC Laboratories America, Inc.
Inventor： Xiang Yu , Feng Zhou , Manmohan Chandrakar
Abstract: A system and method are provided. The system includes a processor. The processor is configured to generate a response map for an image, using a four stage convolutional structure. The processor is further configured to generate a plurality of landmark points for the image based on the response map, using a shape basis neural network. The processor is additionally configured to generate an optimal shape for the image based on the plurality of landmark points for the image and the response map, using a point deformation neural network. A recognition system configured to identify the image based on the generated optimal shape to generate a recognition result of the image. The processor is also configured to operate a hardware-based machine based on the recognition result.
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