Abstract: A method for recognizing a dynamic gesture, includes: obtaining a plurality of video frames by sampling a video stream; obtaining a plurality of key point features respectively corresponding to the plurality of video frames by performing key point feature detection of human body area on the plurality of video frames respectively; determining dependent features among the plurality of key point features, and a plurality of image features respectively corresponding to the plurality of video frames; and obtaining a target dynamic gesture by recognizing dynamic gestures in the video stream based on the dependent features and the plurality of image features.

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.

Abstract: A human-object interaction detection method, a neural network and a training method therefor is provided.

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.

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.

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.

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.