Huawei Technologies Co., Limited

Beijing Sensetime Technology Development Co., Ltd.

Beijing Institute for Cancer Research

Limin Wang

Xiaoou Tang

Wei Li

Wei Zhao

Yue Zhao

Chunjing Xu

Zhirong Wu

Dahua Lin

Yu Qiao

Yuanjun Xiong

Zhiqian Zhang

Haibo Han

Baocai Xing

Time domain action detecting methods and systems, electronic devices, and computer storage medium are provided. The method includes: obtaining a time domain interval in a video with an action instance and at least one adjacent segment in the time domain interval; separately extracting action features of at least two video segments in candidate segments, where the candidate segments comprises video segment corresponding to the time domain interval and adjacent segments thereof; pooling the action features of the at least two video segments in the candidate segments, to obtain a global feature of the video segment corresponding to the time domain interval; and determining, based on the global feature, an action integrity score of the video segment corresponding to the time domain interval. The embodiments of the present disclosure benefit accurately determining whether a time domain interval comprises an integral action instance, and improve the accuracy rate of action integrity identification.

Time domain action detecting methods and system, electronic devices, and computer storage medium

Disclosed is a method for searching, identifying, or validating a marker CACNA2D1 of tumor-initiating cells. The method comprises a step of immunizing an animal using HEP-12 cells originating from a recurrent tumor and rich in originating cells. Also disclosed is a monoclonal antibody specially recognizing CACNA2D1 or antigen-binding fragments thereof, and the use thereof for treating or preventing tumors or diseases or conditions related to CACNA2D1.

Antibody and binding fragment recognizing CACNA2D1 and use thereof

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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Beijing, China

Limin Wang