Huawei Technologies Co., Limited

Beijing Sensetime Technology Development Co., Ltd.

Yu Qiao

Yali Wang

Jianzhuang Liu

Lihui Jiang

Xiaoou Tang

Lei Zhou

Wei Li

Yue Zhao

Chunjing Xu

Dahua Lin

Limin Wang

Dian Shao

Yu Xiong

Qingqiu Huang

Wenbin Du

An action recognition method and apparatus related to artificial intelligence and include extracting a spatial feature of a to-be-processed picture, determining a virtual optical flow feature of the to-be-processed picture based on the spatial feature and X spatial features and X optical flow features in a preset feature library, where the X spatial features and the X optical flow features include a one-to-one correspondence, determining a first type of confidence of the to-be-processed picture in different action categories based on similarities between the virtual optical flow feature and Y optical flow features, where each of the Y optical flow features in the preset feature library corresponds to one action category, X and Y are both integers greater than 1, and determining an action category of the to-be-processed picture based on the first type of confidence.

Action recognition method and apparatus

Action recognition methods are disclosed. An embodiment of the methods includes: identifying a video that comprises images of a human body to be processed; identifying at least one image to be processed, wherein the at least one image is at least one of an optical flow image generated based on a plurality of frames of images in the video, or a composite image of one or more frames of images in the video; performing convolution on the at least one image to obtain a plurality of eigenvectors, wherein the plurality of eigenvectors indicate a plurality of features of different locations in the at least one image; determining a weight coefficient set of each of a plurality of human joints of the human body based on the plurality of eigenvectors, wherein the weight coefficient set comprises a weight coefficient of each of the plurality of eigenvectors for the human joint; weighting the plurality of eigenvectors based on the weight coefficient set to obtain an action feature of each of the plurality of human joints; determining an action feature of the human body based on the action feature of each of the human joints; and determining an action type of the human body based on the action feature of the human body.

Action recognition and pose estimation method and apparatus

The present disclosure relates to a method and device for video processing, an electronic device, and a storage medium. The method comprises: determining, on the basis of paragraph information of a query text paragraph and video information of multiple videos in a video library, preselected videos associated with the query text paragraph in the multiple videos; and determining a target video in the preselected videos on the basis of video frame information of the preselected videos and of sentence information of the query text paragraph. The method for video processing of the embodiments of the present disclosure indexes videos by means of the relevance between the videos and the query text paragraph, allows the pinpointing of the target video, avoids search result redundancy, allows the processing of the query text paragraph in a natural language form, and is not limited by the inherent contents of content labels.

Method and device for video processing, electronic device, and storage medium

A video classification method and apparatus relate to the field of electronic and information technologies, so that precision of video classification can be improved. The method includes: segmenting a video in a sample video library according to a time sequence, to obtain a segmentation result, and generating a motion atom set; generating, by using the motion atom set and the segmentation result, a motion phrase set that can indicate a complex motion pattern, and generating a descriptive vector, based on the motion phrase set, of the video in the sample video library; and determining, by using the descriptive vector, a to-be-detected video whose category is the same as that of the video in the sample video library. The method is applicable to a scenario of video classification.

Video classification method and apparatus

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Shenzhen, China

Yu Qiao