Computer vision image feature identification via multi-label few-shot model
AssigneeDST Technologies, Inc.
InventorsHui Peng Hu, Ramesh Sridharan
Life of this patent
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Abstract
A technique making use of a few-shot model to determine graphical features present in an image based on a small set of examples with known graphical features. Where a support set including a number of images that each have a known combination of graphical features, the image recognition can identify unknown combinations of those graphical features in any number of query images. In an embodiment of the present disclosure examples of a filled-out form are used to interpret any number of additional filled out versions of the form.
Claims20
A multi-model image content recognition system comprising:
- a first model configured to receive an input set including a query image and a support set of images, wherein first model is trained to output a descriptive vector for each image of the input set including a descriptive query vector and descriptive support vectors;
- a second model configured to receive each descriptive support vector concatenated with the descriptive query vector, wherein the second model is trained to output a comparison vector for each input, the comparison vector indicating a degree of similarity between the query image and a respective member of the support set of images;
- and
- a third model trained to output a solution vector indicating graphic features present in the query image based on the comparison vectors.
A method comprising:
- receiving, by a first model, an input set including a query image and a support set of images;
- outputting, by the first model, a descriptive vector for each image of the input set including a descriptive query vector and descriptive support vectors;
- receiving, by a second model, each descriptive support vector concatenated with the descriptive query vector;
- outputting, by the second model, a comparison vector for each descriptive support vector concatenated with the descriptive query vector, the comparison vector indicating a degree of similarity between the query image and a respective member of the support set of images;
- and
- projecting, by a third model, a solution vector indicating graphic features present in the query image based on the comparison vectors.
A method comprising:
- receiving, by a series of neural network models, an input set including a query image and a support set of images, wherein the support set of images includes at least one instance of each of a set of graphic features and the query image includes an unknown combination of the graphic features that is not present within the support set of images;
- generating, by the series of neural network models, a set of partial products that progressively develop a list of graphic features included in the unknown combination of graphic features of the query image, wherein the series of neural network models vectorizes the input set and then compares and projects the partial products;
- and
- outputting, by the series of neural network models, the list of graphic features included in the unknown combination of graphic features of the query image.
Dependencies are inferred from the claim text.