Systems and methods for few shot object detection
AssigneeCACI, Inc.—Federal
InventorsTyler Staudinger, Ross Massey
Life of this patent
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Abstract
A system may be configured to detect an unseen object. Some embodiments may: train a machine learning (ML) model, with training data and with both a positive-support content item and a negative-support content item; and predict, via the trained ML model, presence, within a region, of an object in a newly-obtained content item. The object may (i) not have previously been used to train the ML model and (ii) be among a background and a candidate object present in the newly-obtained content item.
Claims20
A method for detecting an unseen object, the method comprising:
- obtaining a positive-support content item and a plurality of training data, each datum comprising one or more content items;
- training a machine learning model with the training data and the positive-support content item;
- and
- predicting, via the trained machine learning model, presence, within a region, of an object in a newly-obtained content item,
- wherein the object (i) has not previously been used to train the model and (ii) is among a background and a candidate object present in the newly-obtained content item.
A method for detecting an unseen object, the method comprising:
- first-detecting, via a first machine learning model from among each of a plurality of images, a region of each of at least the unseen object and a candidate object;
- second-detecting, via a second machine learning model operably coupled to the first machine learning model, and from among the first-detected regions, an object having a regional similarity score that satisfies a criterion with respect to a positive-support content item;
- and
- displaying, via a user interface in the plurality of images, only bounds of the region in which the second-detected object is present.
A method, comprising:
- obtaining a first positive-support image and a second positive-support image;
- obtaining a real-time video stream;
- training a machine learning model with training data and the images;
- tagging the stream with the first positive-support image such that the model predicts presence of a first object, while the video stream is being played;
- and
- subsequently tagging, in real-time, the stream with the second positive-support image such that the model predicts presence of a second object,
- wherein neither the first nor second object has been used to perform the training, and wherein the first and second objects are of a different type.
Dependencies are inferred from the claim text.