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1. 发明申请

US20180137406A1 Efficient Convolutional Neural Networks and Techniques to Reduce Associated Computational Costs 审中-公开
公开(公告)号：US20180137406A1
公开(公告)日：2018-05-17
申请号：US15707064
申请日：2017-09-18
申请人： Google Inc.
发明人： Andrew Gerald Howard , Bo Chen , Dmitry Kalenichenko , Tobias Christoph Weyand , Menglong Zhu , Marco Andreetto , Weijun Wang
IPC分类号： G06N3/04 , G06N3/08 , G06T7/32
CPC分类号： G06N3/04 , G06N3/0454 , G06N3/082 , G06N3/084 , G06T7/32 , G06T2207/20081 , G06T2207/20084
摘要： The present disclosure provides systems and methods to reduce computational costs associated with convolutional neural networks. In addition, the present disclosure provides a class of efficient models termed “MobileNets” for mobile and embedded vision applications. MobileNets are based on a straight-forward architecture that uses depthwise separable convolutions to build light weight deep neural networks. The present disclosure further provides two global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the entity building the model to select the appropriately sized model for the particular application based on the constraints of the problem. MobileNets and associated computational cost reduction techniques are effective across a wide range of applications and use cases.

2. 发明授权

US09069794B1 Determining location information for images using landmark, caption, and metadata location data 有权
标题翻译：使用地标，标题和元数据位置数据确定图像的位置信息
公开(公告)号：US09069794B1
公开(公告)日：2015-06-30
申请号：US14052157
申请日：2013-10-11
申请人： Google Inc.
发明人： Farhana Bandukwala , Hartwig Adam , John Flynn , Krzysztof Sikora , Marco Andreetto , Arcot J. Preetham
IPC分类号： G06F17/30 , G06K9/46
CPC分类号： G06F17/30256 , G06F17/30265 , G06K9/46
摘要： Methods, systems, and apparatus are provided for determining location information for images. In one aspect, a method includes obtaining landmark location data from content depicted in an image and corresponding confidence scores. Also, the method includes obtaining caption location data from user input and corresponding confidence scores, and obtaining metadata location data from data provided by an image capturing device. Further, the method includes identifying location pairs from the landmark, caption, and metadata location data, and generating, for each location pair, a geographic consistency score. Additionally, the method includes selecting a location pair based on the geographic consistency scores, and selecting an image location for the image from the selected location pair. Moreover, the method includes determining an image location score based on a confidence score for one of the locations in the selected location pair, and associating the image location and image location score with the image.
摘要翻译：提供了用于确定图像的位置信息的方法，系统和装置。一方面，一种方法包括从图像中描绘的内容和相应的置信度得分获得界标位置数据。此外，该方法包括从用户输入获得字幕位置数据和相应的置信度分数，以及从由图像捕获设备提供的数据获得元数据位置数据。此外，该方法包括从地标，标题和元数据位置数据识别位置对，并且针对每个位置对，生成地理一致性分数。另外，该方法包括基于地理一致性分数来选择位置对，以及从所选位置对中选择图像的图像位置。此外，该方法包括基于所选位置对中的一个位置的置信度分数来确定图像位置分数，以及将图像位置和图像位置分数与图像相关联。

3. 发明授权

US11157814B2 Efficient convolutional neural networks and techniques to reduce associated computational costs 有权
公开(公告)号：US11157814B2
公开(公告)日：2021-10-26
申请号：US15707064
申请日：2017-09-18
申请人： Google Inc.
发明人： Andrew Gerald Howard , Bo Chen , Dmitry Kalenichenko , Tobias Christoph Weyand , Menglong Zhu , Marco Andreetto , Weijun Wang
IPC分类号： G06N3/08 , G06N3/04
摘要： The present disclosure provides systems and methods to reduce computational costs associated with convolutional neural networks. In addition, the present disclosure provides a class of efficient models termed “MobileNets” for mobile and embedded vision applications. MobileNets are based on a straight-forward architecture that uses depthwise separable convolutions to build light weight deep neural networks. The present disclosure further provides two global hyper-parameters that efficiently trade-off between latency and accuracy. These hyper-parameters allow the entity building the model to select the appropriately sized model for the particular application based on the constraints of the problem. MobileNets and associated computational cost reduction techniques are effective across a wide range of applications and use cases.

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