Patents by Inventor Xiangbo Shu

Xiangbo Shu has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Weakly supervised video activity detection method and system based on iterative learning
    Patent number: 11721130
    Abstract: The present disclosure relates to a weakly supervised video activity detection method and system based on iterative learning. The method includes: extracting spatial-temporal features of a video that contains actions; constructing a neural network model group; training a first neural network model according to the class label of the video, a class activation sequence output by the first neural network model, and a video feature output by the first neural network model; training the next neural network model according to the class label of the video, a pseudo temporal label output by the current neural network model, a class activation sequence output by the next neural network model, and a video feature output by the next neural network model; and performing action detection on the test video according to the neural network model corresponding to the highest detection accuracy value.
    Type: Grant
    Filed: September 16, 2020
    Date of Patent: August 8, 2023
    Assignee: NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY
    Inventors: Yan Song, Rong Zou, Xiangbo Shu
  • WEAKLY SUPERVISED VIDEO ACTIVITY DETECTION METHOD AND SYSTEM BASED ON ITERATIVE LEARNING
    Publication number: 20220189209
    Abstract: The present disclosure relates to a weakly supervised video activity detection method and system based on iterative learning. The method includes: extracting spatial-temporal features of a video that contains actions; constructing a neural network model group; training a first neural network model according to the class label of the video, a class activation sequence output by the first neural network model, and a video feature output by the first neural network model; training the next neural network model according to the class label of the video, a pseudo temporal label output by the current neural network model, a class activation sequence output by the next neural network model, and a video feature output by the next neural network model; and performing action detection on the test video according to the neural network model corresponding to the highest detection accuracy value.
    Type: Application
    Filed: September 16, 2020
    Publication date: June 16, 2022
    Inventors: Yan Song, Rong Zou, Xiangbo Shu