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公开(公告)号：US20230052389A1
公开(公告)日：2023-02-16
申请号：US17976662
申请日：2022-10-28
Inventor： Desen ZHOU , Jian WANG , Hao SUN
Abstract: A human-object interaction detection method, a neural network and a training method therefor is provided. The human-object interaction detection method includes: extracting a plurality of first target features and one or more first motion features from an image feature of an image to be detected; fusing each first target feature and some of the first motion features to obtain enhanced first target features; fusing each first motion feature and some of the first target features to obtain enhanced first motion features; processing the enhanced first target features to obtain target information of a plurality of targets including human targets and object targets; processing the enhanced first motion features to obtain motion information of one or more motions, where each motion is associated with one human target and one object target; and matching the plurality of targets with the one or more motions to obtain a human-object interaction detection result.
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Patent Agency Ranking
公开(公告)号：US20220360796A1
公开(公告)日：2022-11-10
申请号：US17870660
申请日：2022-07-21
Inventor： Desen ZHOU , Jian Wang , Hao Sun
IPC: H04N19/172 , H04N19/132
Abstract: A method and apparatus for recognizing an action. The method includes: acquiring a target video; determining action categories corresponding to the target video; determining, for each action category, a pre-action-conversion video frame and post-action-conversion video frame corresponding to the action category from the target video; and determining a number of actions corresponding to the each action category based on the pre-action-conversion video frame and post-action-conversion video frame corresponding to the each action category.
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公开(公告)号：US20220222941A1
公开(公告)日：2022-07-14
申请号：US17707657
申请日：2022-03-29
Inventor： Desen ZHOU , Jian WANG , Hao SUN
Abstract: A method for recognizing an action includes: obtaining a sequence for key points; extracting first space-time features corresponding to the sequence; obtaining a second space-time feature corresponding to a time granularity by performing feature extraction on the first space-time features based on the time granularity; and obtaining a target recognized action of the sequence based on second space-time features corresponding to time granularities.
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公开(公告)号：US20230047628A1
公开(公告)日：2023-02-16
申请号：US17976668
申请日：2022-10-28
Inventor： Desen ZHOU , Jian Wang , Hao Sun
Abstract: A human-object interaction detection method, a neural network and a training method therefor is provided. The human-object interaction detection method includes: performing first target feature extraction on image features of an image to obtain first target features; performing first interaction feature extraction on image features to obtain first interaction features and scores thereof; determining at least some first interaction features in the first interaction features based on the score of each of the first interaction features; determining first motion features based on the at least some first interaction features and the image features; processing the first target features to obtain target information of targets in the image; processing the first motion features to obtain motion information of one or more motions in the image; and matching the targets with the motions to obtain a human-object interaction detection result.
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公开(公告)号：US20230102422A1
公开(公告)日：2023-03-30
申请号：US17807375
申请日：2022-06-16
Inventor： Desen ZHOU , Jian WANG , Hao SUN
Abstract: Provided is an image recognition method. The method includes determining subject decoded features of a to-be-detected image and an original interaction decoded feature of a subject interactive relationship in the to-be-detected image; determining subject decoded features associated with the original interaction decoded feature, and updating the original interaction decoded feature by using the associated subject decoded features so as to obtain a new interaction decoded feature; and according to the subject decoded features of the to-be-detected image and the new interaction decoded feature, determining at least two subjects to which the subject interactive relationship in the to-be-detected belongs.
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公开(公告)号：US20230051232A1
公开(公告)日：2023-02-16
申请号：US17976673
申请日：2022-10-28
Inventor： Desen ZHOU , Jian WANG , Hao SUN
Abstract: A human-object interaction detection method, a neural network and a training method therefor is provided. The human-object interaction detection method includes: performing first target feature extraction on an image feature of an image; performing first interaction feature extraction on the image feature; processing a plurality of first target features to obtain target information of a plurality of detected targets; processing one or more first interaction features to obtain motion information of a motion, human information of a human target corresponding to each motion, and object information of an object target corresponding to each motion; matching the plurality of detected targets with one or more motions; and updating human information of a corresponding human target based on target information of a detected target matching the corresponding human target, and updating object information of a corresponding object target based on target information of a detected target matching the corresponding object target.
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