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公开(公告)号：US20240412458A1
公开(公告)日：2024-12-12
申请号：US18741680
申请日：2024-06-12
Applicant: Google LLC
Inventor： Varun Jampani , Chun-Han Yao , Amit Raj , Wei-Chih Hung , Ming-Hsuan Yang , Michael Rubinstein , Yuanzhen Li
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for editing images based on decoder-based accumulative score sampling (DASS) losses.
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公开(公告)号：US11132800B2
公开(公告)日：2021-09-28
申请号：US16591104
申请日：2019-10-02
Applicant: Google LLC
Inventor： Yichang Shih , Chia-Kai Liang , Wei-Sheng Lai , Ming-Hsuan Yang , Siargey Pisarchyk , Ryhor Karpiak
Abstract: Apparatus and methods related to image processing are provided. A computing device can determine a first image area of an image, such as an image captured by a camera. The computing device can determine a warping mesh for the image with a first portion of the warping mesh associated with the first image area. The computing device can determine a cost function for the warping mesh by: determining first costs associated with the first portion of the warping mesh that include costs associated with face-related transformations of the first image area to correct geometric distortions. The computing device can determine an optimized mesh based on optimizing the cost function. The computing device can modify the first image area based on the optimized mesh.
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公开(公告)号：US20210035307A1
公开(公告)日：2021-02-04
申请号：US16591104
申请日：2019-10-02
Applicant: Google LLC
Inventor： Yichang Shih , Chia-Kai Liang , Wei-Sheng Lai , Ming-Hsuan Yang , Siargey Pisarchyk , Ryhor Karpiak
Abstract: Apparatus and methods related to image processing are provided. A computing device can determine a first image area of an image, such as an image captured by a camera. The computing device can determine a warping mesh for the image with a first portion of the warping mesh associated with the first image area. The computing device can determine a cost function for the warping mesh by: determining first costs associated with the first portion of the warping mesh that include costs associated with face-related transformations of the first image area to correct geometric distortions. The computing device can determine an optimized mesh based on optimizing the cost function. The computing device can modify the first image area based on the optimized mesh.
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公开(公告)号：US20240022760A1
公开(公告)日：2024-01-18
申请号：US18256837
申请日：2021-08-05
Applicant: Google LLC
Inventor： Yinxiao Li , Peyman Milanfar , Feng Yang , Ce Liu , Ming-Hsuan Yang , Pengchong Jin
IPC: H04N19/59 , G06T3/00 , H04N19/117 , G06V10/74 , H04N19/503 , H04N19/70 , H04N19/80
CPC classification number: H04N19/59 , G06T3/0093 , H04N19/117 , G06V10/761 , H04N19/503 , H04N19/70 , H04N19/80
Abstract: Example aspects of the present disclosure are directed to systems and methods which feature a machine-learned video super-resolution (VSR) model which has been trained using a bi-directional training approach. In particular, the present disclosure provides a compression-informed (e.g., compression-aware) super-resolution model that can perform well on real-world videos with different levels of compression. Specifically, example models described herein can include three modules to robustly restore the missing information caused by video compression. First, a bi-directional recurrent module can be used to reduce the accumulated warping error from the random locations of the intra-frame from compressed video frames. Second, a detail-aware flow estimation module can be added to enable recovery of high resolution (HR) flow from compressed low resolution (LR) frames. Finally, a Laplacian enhancement module can add high-frequency information to the warped HR frames washed out by video encoding.
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公开(公告)号：US11790550B2
公开(公告)日：2023-10-17
申请号：US17292647
申请日：2020-07-08
Applicant: Google LLC
Inventor： Taihong Xiao , Deqing Sun , Ming-Hsuan Yang , Qifei Wang , Jinwei Yuan
CPC classification number: G06T7/593 , G06T7/215 , G06T2207/10012 , G06T2207/20081
Abstract: A method includes obtaining a first plurality of feature vectors associated with a first image and a second plurality of feature vectors associated with a second image. The method also includes generating a plurality of transformed feature vectors by transforming each respective feature vector of the first plurality of feature vectors by a kernel matrix trained to define an elliptical inner product space. The method additionally includes generating a cost volume by determining, for each respective transformed feature vector of the plurality of transformed feature vectors, a plurality of inner products, wherein each respective inner product of the plurality of inner products is between the respective transformed feature vector and a corresponding candidate feature vector of a corresponding subset of the second plurality of feature vectors. The method further includes determining, based on the cost volume, a pixel correspondence between the first image and the second image.
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Patent Agency Ranking
公开(公告)号：US11443162B2
公开(公告)日：2022-09-13
申请号：US16549715
申请日：2019-08-23
Applicant: Google LLC
Inventor： Ming-Hsuan Yang , Xiaojie Jin , Joshua Foster Slocum , Shengyang Dai , Jiang Wang
IPC: G06N3/04 , G06N20/00 , G06F16/901
Abstract: Methods, and systems, including computer programs encoded on computer storage media for neural network architecture search. A method includes defining a neural network computational cell, the computational cell including a directed graph of nodes representing respective neural network latent representations and edges representing respective operations that transform a respective neural network latent representation; replacing each operation that transforms a respective neural network latent representation with a respective linear combination of candidate operations, where each candidate operation in a respective linear combination has a respective mixing weight that is parameterized by one or more computational cell hyper parameters; iteratively adjusting values of the computational cell hyper parameters and weights to optimize a validation loss function subject to computational resource constraints; and generating a neural network for performing a machine learning task using the defined computational cell and the adjusted values of the computational cell hyper parameters and weights.
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公开(公告)号：US20210056378A1
公开(公告)日：2021-02-25
申请号：US16549715
申请日：2019-08-23
Applicant: Google LLC
Inventor： Ming-Hsuan Yang , Xiaojie Jin , Joshua Foster Slocum , Shengyang Dai , Jiang Wang
IPC: G06N3/04 , G06N20/00 , G06F16/901
Abstract: Methods, and systems, including computer programs encoded on computer storage media for neural network architecture search. A method includes defining a neural network computational cell, the computational cell including a directed graph of nodes representing respective neural network latent representations and edges representing respective operations that transform a respective neural network latent representation; replacing each operation that transforms a respective neural network latent representation with a respective linear combination of candidate operations, where each candidate operation in a respective linear combination has a respective mixing weight that is parameterized by one or more computational cell hyper parameters; iteratively adjusting values of the computational cell hyper parameters and weights to optimize a validation loss function subject to computational resource constraints; and generating a neural network for performing a machine learning task using the defined computational cell and the adjusted values of the computational cell hyper parameters and weights.
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公开(公告)号：US20220414425A1
公开(公告)日：2022-12-29
申请号：US17821076
申请日：2022-08-19
Applicant: Google LLC
Inventor： Ming-Hsuan Yang , Xiaojie Jin , Joshua Foster Slocum , Shengyang Dai , Jiang Wang
IPC: G06N3/04 , G06N20/00 , G06F16/901
Abstract: Methods, and systems, including computer programs encoded on computer storage media for neural network architecture search. A method includes defining a neural network computational cell, the computational cell including a directed graph of nodes representing respective neural network latent representations and edges representing respective operations that transform a respective neural network latent representation; replacing each operation that transforms a respective neural network latent representation with a respective linear combination of candidate operations, where each candidate operation in a respective linear combination has a respective mixing weight that is parameterized by one or more computational cell hyper parameters; iteratively adjusting values of the computational cell hyper parameters and weights to optimize a validation loss function subject to computational resource constraints; and generating a neural network for performing a machine learning task using the defined computational cell and the adjusted values of the computational cell hyper parameters and weights.
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公开(公告)号：US20220189051A1
公开(公告)日：2022-06-16
申请号：US17292647
申请日：2020-07-08
Applicant: Google LLC
Inventor： Taihong Xiao , Deqing Sun , Ming-Hsuan Yang , Qifei Wang , Jinwei Yuan
Abstract: A method includes obtaining a first plurality of feature vectors associated with a first image and a second plurality of feature vectors associated with a second image. The method also includes generating a plurality of transformed feature vectors by transforming each respective feature vector of the first plurality of feature vectors by a kernel matrix trained to define an elliptical inner product space. The method additionally includes generating a cost volume by determining, for each respective transformed feature vector of the plurality of transformed feature vectors, a plurality of inner products, wherein each respective inner product of the plurality of inner products is between the respective transformed feature vector and a corresponding candidate feature vector of a corresponding subset of the second plurality of feature vectors. The method further includes determining, based on the cost volume, a pixel correspondence between the first image and the second image.
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公开(公告)号：US20220058808A1
公开(公告)日：2022-02-24
申请号：US17460831
申请日：2021-08-30
Applicant: Google LLC
Inventor： Yichang Shih , Chia-Kai Liang , Wei-Sheng Lai , Ming-Hsuan Yang , Siargey Pisarchyk , Ryhor Karpiak
Abstract: Apparatus and methods related to image processing are provided. A computing device can determine a first image area of an image, such as an image captured by a camera. The computing device can determine a warping mesh for the image with a first portion of the warping mesh associated with the first image area. The computing device can determine a cost function for the warping mesh by: determining first costs associated with the first portion of the warping mesh that include costs associated with face-related transformations of the first image area to correct geometric distortions. The computing device can determine an optimized mesh based on optimizing the cost function. The computing device can modify the first image area based on the optimized mesh.
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