Patents by Inventor Hexiang Hu
Hexiang Hu 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: 20240114158Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representatiType: ApplicationFiled: December 5, 2023Publication date: April 4, 2024Inventors: Vihan Jain, Joonseok Lee, Ming Zhao, Sheide Chammas, Hexiang Hu, Bowen Zhang, Fei Sha, Tze Way Eugene Ie
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Patent number: 11876986Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representatiType: GrantFiled: November 29, 2022Date of Patent: January 16, 2024Assignee: GOOGLE LLCInventors: Vihan Jain, Joonseok Lee, Ming Zhao, Sheide Chammas, Hexiang Hu, Bowen Zhang, Fei Sha, Tze Way Eugene Ie
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Publication number: 20230103148Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representatiType: ApplicationFiled: November 29, 2022Publication date: March 30, 2023Inventors: Vihan Jain, Joonseok Lee, Ming Zhao, Sheide Chammas, Hexiang Hu, Bowen Zhang, Fei Sha, Tze Way Eugene Ie
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Patent number: 11568545Abstract: Various embodiments of a framework which allow, as an alternative to resource-taxing decompression, efficient computation of feature maps using a compressed content data subset, such as video, by exploiting the motion information, such as a motion vector, present in the compressed video. This framework allows frame-specific object recognition and action detection algorithms to be applied to compressed video and other media files by executing only on I-frames in a Group of Pictures and linearly interpolating the results. Training and machine learning increases recognition accuracy. Yielding significant computational gains, this approach accelerates frame-wise feature extraction I-frame/P-frame/P-frame videos as well as I-frame/P-frame/B-frame videos. The present techniques may also be used for segmentation to identify and label respective regions for objects in a video.Type: GrantFiled: December 27, 2019Date of Patent: January 31, 2023Assignee: A9.com, Inc.Inventors: R. Manmatha, Hexiang Hu, Deva Ramanan
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Patent number: 11533495Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representatiType: GrantFiled: January 29, 2021Date of Patent: December 20, 2022Assignee: GOOGLE LLCInventors: Vihan Jain, Joonseok Lee, Ming Zhao, Sheide Chammas, Hexiang Hu, Bowen Zhang, Fei Sha, Tze Way Eugene Ie
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Publication number: 20220256175Abstract: A computer-implemented method for generating video representations utilizing a hierarchical video encoder includes obtaining a video, wherein the video includes a plurality of frames, processing each of the plurality of frames with a machine-learned frame-level encoder model to respectively generate a plurality of frame representations for the plurality of frames, the plurality of frame representations respective to the plurality of frames determining a plurality of segment representations representative of a plurality of video segments including one or more of the plurality of frames, the plurality of segment representations based at least in part on the plurality of frame representations, processing the plurality of segment representations with a machine-learned segment-level encoder model to generate a plurality of contextualized segment representations, determining a video representation based at least in part on the plurality of contextualized segment representations, and providing the video representatiType: ApplicationFiled: January 29, 2021Publication date: August 11, 2022Inventors: Vihan Jain, Joonseok Lee, Ming Zhao, Sheide Chammas, Hexiang Hu, Bowen Zhang, Fei Sha, Tze Way Eugene Ie
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Publication number: 20210342924Abstract: Various embodiments of a framework which allow, as an alternative to resource-taxing decompression, efficient computation of feature maps using a compressed content data subset, such as video, by exploiting the motion information, such as a motion vector, present in the compressed video. This framework allows frame-specific object recognition and action detection algorithms to be applied to compressed video and other media files by executing only on I-frames in a Group of Pictures and linearly interpolating the results. Training and machine learning increases recognition accuracy. Yielding significant computational gains, this approach accelerates frame-wise feature extraction I-frame/P-frame/P-frame videos as well as I-frame/P-frame/B-frame videos. The present techniques may also be used for segmentation to identify and label respective regions for objects in a video.Type: ApplicationFiled: December 27, 2019Publication date: November 4, 2021Inventors: R. Manmatha, Hexiang Hu, Deva Ramanan
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Publication number: 20200143457Abstract: Various embodiments of a framework which allow, as an alternative to resource-taxing decompression, efficient computation of feature maps using a compressed content data subset, such as video, by exploiting the motion information, such as a motion vector, present in the compressed video. This framework allows frame-specific object recognition and action detection algorithms to be applied to compressed video and other media files by executing only on I-frames in a Group of Pictures and linearly interpolating the results. Training and machine learning increases recognition accuracy. Yielding significant computational gains, this approach accelerates frame-wise feature extraction I-frame/P-frame/P-frame videos as well as I-frame/P-frame/B-frame videos. The present techniques may also be used for segmentation to identify and label respective regions for objects in a video.Type: ApplicationFiled: December 27, 2019Publication date: May 7, 2020Inventors: R. Manmatha, Hexiang Hu, Deva Ramanan
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Patent number: 10528819Abstract: Various embodiments of a framework which allow, as an alternative to resource-taxing decompression, efficient computation of feature maps using a compressed content data subset, such as video, by exploiting the motion information, such as a motion vector, present in the compressed video. This framework allows frame-specific object recognition and action detection algorithms to be applied to compressed video and other media files by executing only on I-frames in a Group of Pictures and linearly interpolating the results. Training and machine learning increases recognition accuracy. Yielding significant computational gains, this approach accelerates frame-wise feature extraction I-frame/P-frame/P-frame videos as well as I-frame/P-frame/B-frame videos. The present techniques may also be used for segmentation to identify and label respective regions for objects in a video.Type: GrantFiled: November 20, 2017Date of Patent: January 7, 2020Assignee: A9.COM, INC.Inventors: R. Manmatha, Hexiang Hu, Deva Ramanan
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Patent number: D1012342Type: GrantFiled: September 30, 2023Date of Patent: January 23, 2024Inventor: Hexiang Hu