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US Patent 10558750 Spatial attention model for image captioning

Patent 10558750 was granted and assigned to Salesforce.com, Inc. on February, 2020 by the United States Patent and Trademark Office.

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Patent
Patent
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Patent Applicant
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1
Current Assignee
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
105587501
Patent Inventor Names
Caiming Xiong1
Jiasen Lu1
Richard Socher1
Date of Patent
February 11, 2020
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Patent Application Number
158171531
Date Filed
November 17, 2017
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Patent Citations
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US Patent 10032498 Memory cell unit and recurrent neural network including multiple memory cell units
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US Patent 10282663 Three-dimensional (3D) convolution with 3D batch normalization
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US Patent 10346721 Training a neural network using augmented training datasets
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US Patent 10013640 Object recognition from videos using recurrent neural networks
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US Patent 10133729 Semantically-relevant discovery of solutions
Patent Citations Received
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US Patent 12086539 System and method for natural language processing using neural network with cross-task training
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US Patent 11487999 Spatial-temporal reasoning through pretrained language models for video-grounded dialogues
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US Patent 11443168 Log analysis system employing long short-term memory recurrent neural net works
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US Patent 11922303 Systems and methods for distilled BERT-based training model for text classification
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US Patent 11934781 Systems and methods for controllable text summarization
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US Patent 11934952 Systems and methods for natural language processing using joint energy-based models
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US Patent 11948665 Systems and methods for language modeling of protein engineering
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US Patent 11481636 Systems and methods for out-of-distribution classification
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Patent Primary Examiner
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Fayyaz Alam
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Patent abstract

The technology disclosed presents a novel spatial attention model that uses current hidden state information of a decoder long short-term memory (LSTM) to guide attention and to extract spatial image features for use in image captioning. The technology disclosed also presents a novel adaptive attention model for image captioning that mixes visual information from a convolutional neural network (CNN) and linguistic information from an LSTM. At each timestep, the adaptive attention model automatically decides how heavily to rely on the image, as opposed to the linguistic model, to emit the next caption word. The technology disclosed further adds a new auxiliary sentinel gate to an LSTM architecture and produces a sentinel LSTM (Sn-LSTM). The sentinel gate produces a visual sentinel at each timestep, which is an additional representation, derived from the LSTM's memory, of long and short term visual and linguistic information.

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