Patents by Inventor Haitian XU
Haitian XU has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240127411Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.Type: ApplicationFiled: October 3, 2022Publication date: April 18, 2024Inventors: Zhe Lin, Haitian Zheng, Elya Shechtman, Jianming Zhang, Jingwan Lu, Ning Xu, Qing Liu, Scott Cohen, Sohrab Amirghodsi
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Publication number: 20240127410Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.Type: ApplicationFiled: October 3, 2022Publication date: April 18, 2024Inventors: Zhe Lin, Haitian Zheng, Elya Shechtman, Jianming Zhang, Jingwan Lu, Ning Xu, Qing Liu, Scott Cohen, Sohrab Amirghodsi
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Publication number: 20240127412Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.Type: ApplicationFiled: October 3, 2022Publication date: April 18, 2024Inventors: Zhe Lin, Haitian Zheng, Elya Shechtman, Jianming Zhang, Jingwan Lu, Ning Xu, Qing Liu, Scott Cohen, Sohrab Amirghodsi
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Publication number: 20240127452Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for panoptically guiding digital image inpainting utilizing a panoptic inpainting neural network. In some embodiments, the disclosed systems utilize a panoptic inpainting neural network to generate an inpainted digital image according to panoptic segmentation map that defines pixel regions corresponding to different panoptic labels. In some cases, the disclosed systems train a neural network utilizing a semantic discriminator that facilitates generation of digital images that are realistic while also conforming to a semantic segmentation. The disclosed systems generate and provide a panoptic inpainting interface to facilitate user interaction for inpainting digital images. In certain embodiments, the disclosed systems iteratively update an inpainted digital image based on changes to a panoptic segmentation map.Type: ApplicationFiled: October 3, 2022Publication date: April 18, 2024Inventors: Zhe Lin, Haitian Zheng, Elya Shechtman, Jianming Zhang, Jingwan Lu, Ning Xu, Qing Liu, Scott Cohen, Sohrab Amirghodsi
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Patent number: 8620655Abstract: A speech processing method, comprising: receiving a speech input which comprises a sequence of feature vectors; determining the likelihood of a sequence of words arising from the sequence of feature vectors using an acoustic model and a language model, comprising: providing an acoustic model for performing speech recognition on an input signal which comprises a sequence of feature vectors, said model having a plurality of model parameters relating to the probability distribution of a word or part thereof being related to a feature vector, wherein said speech input is a mismatched speech input which is received from a speaker in an environment which is not matched to the speaker or environment under which the acoustic model was trained; and adapting the acoustic model to the mismatched speech input, the speech processing method further comprising determining the likelihood of a sequence of features occurring in a given language using a language model; and combining the likelihoods determined by the acousticType: GrantFiled: August 10, 2011Date of Patent: December 31, 2013Assignee: Kabushiki Kaisha ToshibaInventors: Haitian Xu, Kean Kheong Chin, Mark John Francis Gales
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Patent number: 8595006Abstract: A speech recognition method and system, includes receiving in a first noise environment a speech input having a sequence of observations; determining a likelihood of a sequence of words arising from the sequence of observations using an acoustic model trained to recognize speech in a second noise environment, the model having a plurality of model parameters relating to the probability distribution of a word or part thereof being related to an observation; and adapting the model trained in the second environment to that of the first environment.Type: GrantFiled: March 26, 2010Date of Patent: November 26, 2013Assignee: Kabushiki Kaisha ToshibaInventors: Haitian Xu, Mark John Francis Gales
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Patent number: 8417522Abstract: A speech recognition method includes receiving a speech input signal in a first noise environment which includes a sequence of observations, determining the likelihood of a sequence of words arising from the sequence of observations using an acoustic model, adapting the model trained in a second noise environment to that of the first environment, wherein adapting the model trained in the second environment to that of the first environment includes using second order or higher order Taylor expansion coefficients derived for a group of probability distributions and the same expansion coefficient is used for the whole group.Type: GrantFiled: April 20, 2010Date of Patent: April 9, 2013Assignee: Kabushiki Kaisha ToshibaInventors: Haitian Xu, Kean Kheong Chin
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Publication number: 20120041764Abstract: A speech processing method, comprising: receiving a speech input which comprises a sequence of feature vectors; determining the likelihood of a sequence of words arising from the sequence of feature vectors using an acoustic model and a language model, comprising: providing an acoustic model for performing speech recognition on an input signal which comprises a sequence of feature vectors, said model having a plurality of model parameters relating to the probability distribution of a word or part thereof being related to a feature vector, wherein said speech input is a mismatched speech input which is received from a speaker in an environment which is not matched to the speaker or environment under which the acoustic model was trained; and adapting the acoustic model to the mismatched speech input, the speech processing method further comprising determining the likelihood of a sequence of features occurring in a given language using a language model; and combining the likelihoods determined by the acousticType: ApplicationFiled: August 10, 2011Publication date: February 16, 2012Applicant: KABUSHIKI KAISHA TOSHIBAInventors: Haitian XU, Kean Kheong Chin, Mark John Francis Gales
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Publication number: 20110015925Abstract: A speech recognition method, comprising: receiving a speech input in a first noise environment which comprises a sequence of observations; determining the likelihood of a sequence of words arising from the sequence of observations using an acoustic model, comprising: providing an acoustic model for performing speech recognition on a input signal which comprises a sequence of observations, wherein said model has been trained to recognise speech in a second noise environment, said model having a plurality of model parameters relating to the probability distribution of a word or part thereof being related to an observation; adapting the model trained in the second environment to that of the first environment; the speech recognition method further comprising determining the likelihood of a sequence of observations occurring in a given language using a language model; combining the likelihoods determined by the acoustic model and the language model and outputting a sequence of words identified from said speeType: ApplicationFiled: March 26, 2010Publication date: January 20, 2011Applicant: Kabushiki Kaisha ToshibaInventors: Haitian Xu, Mark John Francis Gales
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Publication number: 20100204988Abstract: A speech recognition method includes receiving a speech input signal in a first noise environment which includes a sequence of observations, determining the likelihood of a sequence of words arising from the sequence of observations using an acoustic model, adapting the model trained in a second noise environment to that of the first environment, wherein adapting the model trained in the second environment to that of the first environment includes using second order or higher order Taylor expansion coefficients derived for a group of probability distributions and the same expansion coefficient is used for the whole group.Type: ApplicationFiled: April 20, 2010Publication date: August 12, 2010Inventors: Haitian XU, Kean Kheong Chin