SYSTEM AND METHOD FOR FEW-SHOT LEARNING

1 . A system, comprising:

a storage device; and

a processor, configured to:

retrieve, from the storage device, a training set of images in which objects of a particular class are identified,

using the training set, train a model to identify other objects of the class,

using the trained model, identify candidate objects of the class in respective other images,

subsequently to identifying the candidate objects, augment the training set by, for each image of at least some of the other images:

calculating a feature vector describing at least one candidate object identified in the image,

calculating a score quantifying a similarity between the feature vector and another feature vector describing the identified objects in the training set, and

provided that the score passes a predefined threshold, adding the image to the training set, and

using the augmented training set, retrain the model.

2 . The system according to claim 1 , wherein the processor is further configured to initialize the model using a pre-trained model for identifying objects of other classes, by causing the model to recognize each of the other classes as being not of the particular class.

3 . The system according to claim 1 , wherein the processor is configured to identify each candidate object of the candidate objects by identifying a portion of one of the other images that contains the candidate object.

4 . The system according to claim 1 , wherein the processor is configured to identify each candidate object of the candidate objects by segmenting the candidate object.

5 . The system according to claim 1 , wherein, prior to being augmented, the training set includes fewer than 10 images.

6 . The system according to claim 1 , wherein the model includes a convolutional neural network.

7 . The system according to claim 1 , wherein the processor is configured to compute the score by computing a cosine-similarity score.

8 . The system according to claim 1 , wherein the other feature vector is an average of respective object feature vectors describing the identified objects, respectively.

9 . A method, comprising:

using a training set of images in which objects of a particular class are identified, training a model to identify other objects of the class;

using the trained model, identifying candidate objects of the class in respective other images;

subsequently to identifying the candidate objects, augmenting the training set by, for each image of at least some of the other images:

calculating a feature vector describing at least one candidate object identified in the image,

calculating a score quantifying a similarity between the feature vector and another feature vector describing the identified objects in the training set, and

provided that the score passes a predefined threshold, adding the image to the training set; and

using the augmented training set, retraining the model.

10 . The method according to claim 9 , further comprising initializing the model using a pre-trained model for identifying objects of other classes, by causing the model to recognize each of the other classes as being not of the particular class.

11 . The method according to claim 9 , wherein identifying each candidate object of the candidate objects comprises identifying the candidate object by identifying a portion of one of the other images that contains the candidate object.

12 . The method according to claim 9 , wherein identifying each candidate object of the candidate objects comprises identifying the candidate object by segmenting the candidate object.

13 . The method according to claim 9 , wherein, prior to being augmented, the training set includes fewer than 10 images.

14 . The method according to claim 9 , wherein the model includes a convolutional neural network.

15 . The method according to claim 9 , wherein calculating the score comprises calculating a cosine-similarity score.

16 . The method according to claim 9 , wherein the other feature vector is an average of respective object feature vectors describing the identified objects, respectively.

17 . A computer software product comprising a tangible non-transitory computer-readable medium in which program instructions are stored, which instructions, when read by a processor, cause the processor to:

using a training set of images in which objects of a particular class are identified, train a model to identify other objects of the class,

using the trained model, identify candidate objects of the class in respective other images,

subsequently to identifying the candidate objects, augment the training set by, for each image of at least some of the other images:

calculating a feature vector describing at least one candidate object identified in the image,

calculating a score quantifying a similarity between the feature vector and another feature vector describing the identified objects in the training set, and

provided that the score passes a predefined threshold, adding the image to the training set, and

using the augmented training set, retrain the model.

18 . The computer software product according to claim 17 , further comprising initializing the model using a pre-trained model for identifying objects of other classes, by causing the model to recognize each of the other classes as being not of the particular class.

19 . The computer software product according to claim 17 , wherein the instructions cause the processor to identify each candidate object of the candidate objects by identifying a portion of one of the other images that contains the candidate object.

20 . The computer software product according to claim 17 , wherein the instructions cause the processor to identify each candidate object of the candidate objects by segmenting the candidate object.

21 . (canceled)

22 . (canceled)

23 . (canceled)

24 . (canceled)