Edge-based adaptive machine learning for object recognition

1. A computer-implemented method for adaptive model training, the method comprising:

receiving, by a processing system, an adaption task for object recognition at a target visual domain;

determining, by the processing system, whether an existing adaptive model stored in a model repository is trained to recognize objects of the target visual domain, wherein the determining is based on whether a location of the target visual domain overlaps a location of a visual domain of the existing adaptive model;

generating, by the processing system, a training instance based at least in part on visual objects found in a plurality of clustered images stored on a first worker user's electronic device, wherein the clustered images match a contextual specification of the target visual domain, wherein the contextual specification is received from a master user's electronic device via an adaptation task, wherein generating the training instance is based on determining that the the existing adaptive model is not trained to recognize objects of the target visual domain, and wherein generating the training instance comprises:

generating, by the processing system, a plurality of candidate labels, wherein the labels are associated with the target visual domain;

extracting, by the processing system, objects from one of the plurality of images stored on the first worker user's electronic device;

receiving, by the processor system, a recommended label selected from the plurality of candidate labels for each respective extracted object;

extracting, by the processing system, a respective plurality of machine learning model features describing each respective extractive object, from a hidden layer of a machine learning model stored on the first worker user's electronic device, and simultaneous to a selection of the recommended label;

transmitting, by the processor, the respective plurality of extracted machine learning model features and recommended labels to the master user's electronic device to train an adaptive model.

2. The computer-implemented method of claim 1 further comprising, based at least in part on presenting the machine learning model features and label recommendations to a first worker user, receiving, by the processing system, the selected recommended label.

3. The computer-implemented method of claim 2 , wherein the machine learning model features and the selected recommended label form the training instance.

4. The computer-implemented method of claim 3 further comprising training an adapted model corresponding to the target visual domain based at least in part on the machine learning features extracted from the images of first worker user's electronic device, and based at least in part on the selected recommended label.

5. The computer-implemented method of claim 1 , wherein the training instance is stored in an adaptation database.

6. The computer-implemented method of claim 5 , wherein the adapted model is stored in the adaptation database.

7. The computer-implemented method of claim 1 , wherein:

the contextual specification comprises a set of attributes; and

the set of attributes includes one or more of location information, time information, and weather information.

8. The computer-implemented method of claim 1 , wherein generating the plurality of machine learning model features and label recommendations further comprises:

inputting object images into a generic machine learning model;

outputting, for each object image of the object images, a respective output vector represented by the probabilities of the object image belonging to different recognizable classes; and

obtaining a final recognition result via a late fusion technique.

9. The computer-implemented method of claim 1 further comprising:

receiving an adaptation task describing the target visual domain by the set of context attributes; and

determining whether to create the adapted model based at least in part on the target visual domain.

10. The computer-implemented method of claim 1 further comprising:

analyzing an adaptation database to determine whether an adapted model exists for the target visual domain; and

determining whether to create the adapted model based at least in part on the determination of whether an adapted model exists for the target visual domain.

11. The computer-implemented method of claim 10 , comprising requesting the first worker user electronic device assist in creating the adapted model based at least a part on a determination to create the adapted model.

12. The computer-implemented method of claim 1 , wherein the adapted model is a support vector machine (SVM).