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ACTIVE LEARNING METHOD FOR TEMPORAL ACTION LOCALIZATION IN UNTRIMMED VIDEOS

  • US 20190325275A1
  • Filed: 04/19/2018
  • Published: 10/24/2019
  • Est. Priority Date: 04/19/2018
  • Status: Active Grant
First Claim
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1. A computer-implemented method for training a localization model that comprises a neural network and identifies a temporal location of an action in a video stream, the method comprising:

  • training, by a computer system, the localization model based on a set of labeled video samples;

    for each unlabeled video sample in a set of unlabeled video samples, determining, by the computer system based on a trainable selection function, a predicted performance improvement of the localization model associated with retraining the localization model;

    selecting, by the computer system based on the predicted performance improvement of the localization model, a first unlabeled video sample from the set of unlabeled video samples;

    receiving by the computer system, a first annotation to the first unlabeled video sample, wherein the first annotation and the first unlabeled video sample form a first labeled video sample; and

    retraining, by the computer system, the localization model based on the set of labeled video samples and the first labeled video sample, wherein an updated localization model is generated upon completion of the retraining.

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