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公开(公告)号：US20190171871A1
公开(公告)日：2019-06-06
申请号：US16236974
申请日：2018-12-31
Applicant: Facebook, Inc.
Inventor： Peizhao Zhang , Peter Vajda , Kevin Matzen , Ross Girshick
Abstract: In one embodiment, a system may access first, second, and third probability models that are respectively associated with predetermined first and second body parts and a predetermined segment connecting the first and second body parts. Each model includes probability values associated with regions in an image, with each value representing the probability of the associated region containing the associated body part or segment. The system may select a first and second region based on the first probability model and a third region based on the second probability model. Based on the third probability model, the system may compute a first probability score for regions connecting the first and third regions and a second probability score for regions connecting the second and third regions. Based on the first and second probability scores, the system may select the first region to indicate where the predetermined first body part appears in the image.
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公开(公告)号：US10733431B2
公开(公告)日：2020-08-04
申请号：US16236974
申请日：2018-12-31
Applicant: Facebook, Inc.
Inventor： Peizhao Zhang , Peter Vajda , Kevin Matzen , Ross Girshick
Abstract: In one embodiment, a system may access first, second, and third probability models that are respectively associated with predetermined first and second body parts and a predetermined segment connecting the first and second body parts. Each model includes probability values associated with regions in an image, with each value representing the probability of the associated region containing the associated body part or segment. The system may select a first and second region based on the first probability model and a third region based on the second probability model. Based on the third probability model, the system may compute a first probability score for regions connecting the first and third regions and a second probability score for regions connecting the second and third regions. Based on the first and second probability scores, the system may select the first region to indicate where the predetermined first body part appears in the image.
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公开(公告)号：US10713794B1
公开(公告)日：2020-07-14
申请号：US15922734
申请日：2018-03-15
Applicant: Facebook, Inc.
Inventor： Kaiming He , Georgia Gkioxari , Piotr Dollar , Ross Girshick
Abstract: In one embodiment, a method includes a computing system accessing a training image. The system may generate a feature map for the training image using a first neural network. The system may identify a region of interest in the feature map and generate a regional feature map for the region of interest based on sampling locations defined by a sampling region. The sampling region and the region of interest may correspond to the same region in the feature map. The system may generate an instance segmentation mask associated with the region of interest by processing the regional feature map using a second neural network. The second neural network may be trained using the instance segmentation mask. Once trained, the second neural network is configured to generate instance segmentation masks for object instances depicted in images.
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公开(公告)号：US20190156210A1
公开(公告)日：2019-05-23
申请号：US16192649
申请日：2018-11-15
Applicant: Facebook, Inc.
Inventor： Kaiming He , Ross Girshick , Xiaolong Wang
Abstract: In one embodiment, a method includes training a baseline machine-learning model based on a neural network comprising a plurality of stages, wherein each stage comprises a plurality of neural blocks, accessing a plurality of training samples comprising a plurality of content objects, respectively, determining one or more non-local operations, wherein each non-local operation is based on one or more pairwise functions and one or more unary functions, generating one or more non-local blocks based on the plurality of training samples and the one or more non-local operations, determining a stage from the plurality of stages of the neural network, and training a non-local machine-learning model by inserting each of the one or more non-local blocks in between at least two of the plurality of neural blocks in the determined stage of the neural network.
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