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1.
公开(公告)号:US20190155877A1
公开(公告)日:2019-05-23
申请号:US15816976
申请日:2017-11-17
Applicant: Adobe Inc.
Inventor: Saumitra Sharma , Kundan Krishna , Balaji Vasan Srinivasan , Aniket Murhekar
CPC classification number: G06F17/2264 , G06F16/345 , G06F17/274 , G06F17/2785 , G06F17/2795 , G06F17/2854 , G06N3/0445 , G06N3/0454 , G06N3/08 , G06N20/00
Abstract: A targeted summary of textual content tuned to a target audience vocabulary is generated in a digital medium environment. A word generation model obtains textual content, and generates a targeted summary of the textual content. During the generation of the targeted summary, the words of the targeted summary generated by the word generation model are tuned to the target audience vocabulary using a linguistic preference model. The linguistic preference model is trained, using machine learning on target audience training data corresponding to a corpus of text of the target audience vocabulary, to learn word preferences of the target audience vocabulary between similar words (e.g., synonyms). After each word is generated using the word generation model and the linguistic preference model, feedback regarding the generated word is provided back to the word generation model. The feedback is utilized by the word generation model to generate subsequent words of the summary.
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2.
公开(公告)号:US20190266228A1
公开(公告)日:2019-08-29
申请号:US16407596
申请日:2019-05-09
Applicant: Adobe Inc.
Inventor: Saumitra Sharma , Kundan Krishna , Balaji Vasan Srinivasan , Aniket Murhekar
Abstract: A targeted summary of textual content tuned to a target audience vocabulary is generated in a digital medium environment. A word generation model obtains textual content, and generates a targeted summary of the textual content. During the generation of the targeted summary, the words of the targeted summary generated by the word generation model are tuned to the target audience vocabulary using a linguistic preference model. The linguistic preference model is trained, using machine learning on target audience training data corresponding to a corpus of text of the target audience vocabulary, to learn word preferences of the target audience vocabulary between similar words (e.g., synonyms). After each word is generated using the word generation model and the linguistic preference model, feedback regarding the generated word is provided back to the word generation model. The feedback is utilized by the word generation model to generate subsequent words of the summary.
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公开(公告)号:US20210067782A1
公开(公告)日:2021-03-04
申请号:US16556744
申请日:2019-08-30
Applicant: Adobe Inc.
Inventor: Saumitra Sharma , Sunil Kumar
IPC: H04N19/132 , H04N19/33 , H04N19/625 , H04N19/80 , H04N19/176 , H04N19/119
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital images of modified resolution by filtering in the frequency domain. For example, the disclosed systems can utilize a tiling procedure to generate discrete cosine transform blocks. The disclosed systems can further filter the quantized data of the discrete cosine transform blocks within the frequency domain using, for example, a Lanczos resampling kernel. In addition, the digital image resolution modification system can utilize sub-band approximation and block composition to generate a modified digital image.
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公开(公告)号:US11102487B2
公开(公告)日:2021-08-24
申请号:US16556744
申请日:2019-08-30
Applicant: Adobe Inc.
Inventor: Saumitra Sharma , Sunil Kumar
IPC: G06K9/00 , H04N19/132 , H04N19/33 , H04N19/119 , H04N19/80 , H04N19/176 , H04N19/625
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating digital images of modified resolution by filtering in the frequency domain. For example, the disclosed systems can utilize a tiling procedure to generate discrete cosine transform blocks. The disclosed systems can further filter the quantized data of the discrete cosine transform blocks within the frequency domain using, for example, a Lanczos resampling kernel. In addition, the digital image resolution modification system can utilize sub-band approximation and block composition to generate a modified digital image.
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公开(公告)号:US10534854B2
公开(公告)日:2020-01-14
申请号:US16407596
申请日:2019-05-09
Applicant: Adobe Inc.
Inventor: Saumitra Sharma , Kundan Krishna , Balaji Vasan Srinivasan , Aniket Murhekar
Abstract: A targeted summary of textual content tuned to a target audience vocabulary is generated in a digital medium environment. A word generation model obtains textual content, and generates a targeted summary of the textual content. During the generation of the targeted summary, the words of the targeted summary generated by the word generation model are tuned to the target audience vocabulary using a linguistic preference model. The linguistic preference model is trained, using machine learning on target audience training data corresponding to a corpus of text of the target audience vocabulary, to learn word preferences of the target audience vocabulary between similar words (e.g., synonyms). After each word is generated using the word generation model and the linguistic preference model, feedback regarding the generated word is provided back to the word generation model. The feedback is utilized by the word generation model to generate subsequent words of the summary.
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公开(公告)号:US10409898B2
公开(公告)日:2019-09-10
申请号:US15816976
申请日:2017-11-17
Applicant: Adobe Inc.
Inventor: Saumitra Sharma , Kundan Krishna , Balaji Vasan Srinivasan , Aniket Murhekar
Abstract: A targeted summary of textual content tuned to a target audience vocabulary is generated in a digital medium environment. A word generation model obtains textual content, and generates a targeted summary of the textual content. During the generation of the targeted summary, the words of the targeted summary generated by the word generation model are tuned to the target audience vocabulary using a linguistic preference model. The linguistic preference model is trained, using machine learning on target audience training data corresponding to a corpus of text of the target audience vocabulary, to learn word preferences of the target audience vocabulary between similar words (e.g., synonyms). After each word is generated using the word generation model and the linguistic preference model, feedback regarding the generated word is provided back to the word generation model. The feedback is utilized by the word generation model to generate subsequent words of the summary.
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