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公开(公告)号：US11887270B2
公开(公告)日：2024-01-30
申请号：US17787699
申请日：2021-07-01
Applicant: Google LLC
Inventor： Junjie Ke , Feng Yang , Qifei Wang , Yilin Wang , Peyman Milanfar
CPC classification number: G06T3/0012 , G06T3/40 , G06T7/0002 , G06T2207/20016 , G06T2207/20081 , G06T2207/30168
Abstract: The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi-scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.
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公开(公告)号：US20250054322A1
公开(公告)日：2025-02-13
申请号：US18787616
申请日：2024-07-29
Applicant: Google LLC
Inventor： Keren Ye , Yicheng Zhu , Junjie Ke , Jiahui Yu , Leonidas John Guibas , Peyman Milanfar , Feng Yang
IPC: G06V20/70 , G06F40/279
Abstract: Systems and methods for attribute recognition can include obtaining an image and a text string. The text string can be processed with a language model to generate a set of candidate attributes based on sequence based prediction. The image and the candidate attributes can be processed with an image-text model to determine a likelihood that the respective candidate attribute is depicted in the image. The likelihood determination can then be utilized to determine a predicted attribute for the object of interest.
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公开(公告)号：US12217382B2
公开(公告)日：2025-02-04
申请号：US18527528
申请日：2023-12-04
Applicant: Google LLC
Inventor： Junjie Ke , Feng Yang , Qifei Wang , Yilin Wang , Peyman Milanfar
Abstract: The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi-scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.
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公开(公告)号：US20240119555A1
公开(公告)日：2024-04-11
申请号：US18527528
申请日：2023-12-04
Applicant: Google LLC
Inventor： Junjie Ke , Feng Yang , Qifei Wang , Yilin Wang , Peyman Milanfar
CPC classification number: G06T3/0012 , G06T3/40 , G06T7/0002 , G06T2207/20016 , G06T2207/20081 , G06T2207/30168
Abstract: The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi-scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.
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公开(公告)号：US12277693B2
公开(公告)日：2025-04-15
申请号：US17612372
申请日：2020-08-06
Applicant: GOOGLE LLC
Inventor： Catherine Shyu , Xiyang Luo , Feng Yang , Junjie Ke , Yicong Tian , Chao-Hung Chen , Xia Li , Luying Li , Wenjing Kang , Shun-Chuan Chen
Abstract: Systems, devices, methods, and computer readable medium for evaluating visual quality of digital content are disclosed. Methods can include training machine learning models on images. A request is received to evaluate quality of an image included in a current version of a digital component generated by the computing device. The machine learning models are deployed on the image to generate a score for each quality characteristic of the image. A weight is assigned to each score to generate weighted scores. The weighted scores are combined to generate a combined score for the image. The combined score is compared to one or more thresholds to generate a quality of the image.
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Patent Agency Ranking
公开(公告)号：US20240346546A1
公开(公告)日：2024-10-17
申请号：US18584716
申请日：2024-02-22
Applicant: Google LLC
Inventor： Catherine Shyu , Luying Li , Feng Yang , Junjie Ke , Xiyang Luo , Hao Feng , Chao-Hung Chen , Wenjing Kang , Zheng Xia , Shun-Chuan Chen , Yicong Tian , Xia Li , Han Ke
IPC: G06Q30/0242
CPC classification number: G06Q30/0244
Abstract: Systems, devices, methods, and computer readable medium for evaluating visual quality of digital content are disclosed. Methods can include identifying content assets including one or more images that are combined to create different digital components distributed to one or more client devices. A quality of each of the one or more images is evaluated using one or more machine learning models trained to evaluate one or more visual aspects that are deemed indicative of visual quality. An aggregate quality for the content assets is determined based, at least in part, on an output of the one or more machine learning models indicating the visual quality of each of the one or more images. A graphical user interface of a first computing device is updated to present a visual indication of the aggregate quality of the content assets.
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7.发明授权公开(公告)号：US12206914B2
公开(公告)日：2025-01-21
申请号：US18021636
申请日：2022-06-08
Applicant: Google LLC
Inventor： Yilin Wang , Balineedu Adsumilli , Junjie Ke , Hossein Talebi , Joong Yim , Neil Birkbeck , Peyman Milanfar , Feng Yang
IPC: H04N21/266 , G06N3/045 , H04N17/02 , H04N19/154 , H04N21/234 , H04N21/434 , H04N21/44 , H04N21/466
Abstract: Methods, systems, and media for determining perceptual quality indicators of video content items are provided. In some embodiments, the method comprises: receiving a video content item; extracting a plurality of frames from the video content item; determining, using a first subnetwork of a deep neural network, a content quality indicator for each frame of the plurality of frames of the video content item; determining, using a second subnetwork of the deep neural network, a video distortion indicator for each frame of the plurality of frames of the video content item; determining, using a third subnetwork of the deep neural network, a compression sensitivity indicator for each frame of the plurality of frames of the video content item; generating a quality level for each frame of the plurality of frames of the video content item that concatenates the content quality indicator, the video distortion indicator, and the compression sensitivity indicator for that frame of the video content item; generating an overall quality level for video content item by aggregating the quality level of each frame of the plurality of frames; and causing a video recommendation to be presented based on the overall quality level of the video content item.
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公开(公告)号：US20230267307A1
公开(公告)日：2023-08-24
申请号：US18014314
申请日：2020-07-23
Applicant: Google LLC
Inventor： Qifei Wang , Junjie Ke , Grace Chu , Gabriel Mintzer Bender , Luciano Sbaiz , Feng Yang , Andrew Gerald Howard , Alec Michael Go , Jeffrey M. Gilbert , Peyman Milanfar , Joshua William Charles Greaves
Abstract: Systems and methods of the present disclosure are directed to a method for generating a machine-learned multitask model configured to perform tasks. The method can include obtaining a machine-learned multitask search model comprising candidate nodes. The method can include obtaining tasks and machine-learned task controller models associated with the tasks. As an example, for a task, the method can include using the task controller model to route a subset of the candidate nodes in a machine-learned task submodel for the corresponding task. The method can include inputting task input data to the task submodel to obtain a task output. The method can include generating, using the task output, a feedback value based on an objective function. The method can include adjusting parameters of the task controller model based on the feedback value.
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公开(公告)号：US20230222623A1
公开(公告)日：2023-07-13
申请号：US17787699
申请日：2021-07-01
Applicant: Google LLC
Inventor： Junjie Ke , Feng Yang , Qifei Wang , Yilin Wang , Peyman Milanfar
CPC classification number: G06T3/0012 , G06T3/40 , G06T7/0002 , G06T2207/30168 , G06T2207/20081 , G06T2207/20016
Abstract: The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi-scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.
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公开(公告)号：US20220358537A1
公开(公告)日：2022-11-10
申请号：US17760535
申请日：2020-08-06
Applicant: Google LLC
Inventor： Catherine Shyu , Luying Li , Feng Yang , Junjie Ke , Xiyang Luo , Hao Feng , Chao-Hung Chen , Wenjing Kang , Zheng Xia , Shun-Chuan Chen , Yicong Tian , Xia Li , Han Ke
IPC: G06Q30/02
Abstract: Systems, devices, methods, and computer readable medium for evaluating visual quality of digital content are disclosed. Methods can include identifying content assets including one or more images that are combined to create different digital components distributed to one or more client devices. A quality of each of the one or more images is evaluated using one or more machine learning models trained to evaluate one or more visual aspects that are deemed indicative of visual quality. An aggregate quality for the content assets is determined based, at least in part, on an output of the one or more machine learning models indicating the visual quality of each of the one or more images. A graphical user interface of a first computing device is updated to present a visual indication of the aggregate quality of the content assets.
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