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公开(公告)号：US12105755B1
公开(公告)日：2024-10-01
申请号：US17852063
申请日：2022-06-28
Applicant: Amazon Technologies, Inc.
Inventor： Mohamed Kamal Omar , Xiaohang Sun , Han-Kai Hsu , Ashutosh Sanan , Wentao Zhu
Abstract: Systems and techniques for retrieving video data associated with a selected attribute are described. The systems and techniques include receiving a multimodal input associated with the attribute for querying a catalog of video data to identify video data including the attribute. A first embedding is determined for the input using a first encoder to map the input to a representation space. A second embedding is determined for the video data to map the video data to the representation space. A similarity score is determined between the video data and the input based on a distance between the embeddings. The video data associated with the attribute may be selected based on the similarity score.
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公开(公告)号：US11589116B1
公开(公告)日：2023-02-21
申请号：US17306671
申请日：2021-05-03
Applicant: Amazon Technologies, Inc.
Inventor： Mohamed Kamal Omar , Xiaohang Sun , Ivan Ryndin , Tai-Ching Li , Alexander Ratnikov , Muhammad Raffay Hamid , Ahmed Aly Saad Ahmed , Travis Silvers , Hanxiao Deng
IPC: H04N21/4545 , H04N21/454 , H04N21/45
Abstract: Techniques are disclosed for detecting a type of prurient activity shown by a portion of video content. In an example, a machine learning model of a computer system may receive a second portion of video content, the machine learning model including a neural network that is trained to analyze a temporal dimension of the second portion. The machine learning model determines a score indicating a likelihood that the video content shows the type of prurient activity based in part on applying a three-dimensional filter to the second portion of the video content. The computer system then generates a video clip that includes at least the portion of the video content showing the type of prurient activity based on the score, and provides the video clip for display.
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公开(公告)号：US12056949B1
公开(公告)日：2024-08-06
申请号：US17215816
申请日：2021-03-29
Applicant: Amazon Technologies, Inc.
Inventor： Xiaohang Sun , Mohamed Kamal Omar , Alexander Ratnikov , Ahmed Aly Saad Ahmed , Tai-Ching Li , Travis Silvers , Hanxiao Deng , Muhammad Raffay Hamid , Ivan Ryndin
CPC classification number: G06V40/10 , G06F18/2178 , G06N3/08 , G06V20/46
Abstract: Techniques are disclosed for detecting an uncovered portion of a body of a person in a frame of video content. In an example, a first machine learning model of a computing system may output a first score for the frame based on a map that identifies a region of the frame associated with an uncovered body part type. Depending on a value of the first score, a second machine learning model that includes a neural network architecture may further analyze the frame to output a second score. The first score and second score may be merged to produce a third score for the frame. A plurality of scores may be determined, respectively, for frames of the video content, and a maximum score may be selected. The video content may be selected for presentation on a display for further evaluation based on the maximum score.
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公开(公告)号：US11928880B1
公开(公告)日：2024-03-12
申请号：US17215816
申请日：2021-03-29
Applicant: Amazon Technologies, Inc.
Inventor： Xiaohang Sun , Mohamed Kamal Omar , Alexander Ratnikov , Ahmed Aly Saad Ahmed , Tai-Ching Li , Travis Silvers , Hanxiao Deng , Muhammad Raffay Hamid , Ivan Ryndin
CPC classification number: G06V40/10 , G06F18/2178 , G06N3/08 , G06V20/46
Abstract: Techniques are disclosed for detecting an uncovered portion of a body of a person in a frame of video content. In an example, a first machine learning model of a computing system may output a first score for the frame based on a map that identifies a region of the frame associated with an uncovered body part type. Depending on a value of the first score, a second machine learning model that includes a neural network architecture may further analyze the frame to output a second score. The first score and second score may be merged to produce a third score for the frame. A plurality of scores may be determined, respectively, for frames of the video content, and a maximum score may be selected. The video content may be selected for presentation on a display for further evaluation based on the maximum score.
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