Abstract: Techniques are generally described for generation of synthetic image data. In some examples, a selection of a first image may be received. The first image may depict at least a first object having a plurality of image attributes representing visual characteristics of the at least the first object. In some examples, a selection of a first image attribute of the plurality of image attributes to be maintained in subsequently-generated images may be received. In various examples, a first machine learning model may generate a second image having the plurality of image attributes. The change in an appearance of the first image attribute may be minimized in the second image while a change in the appearance of other attributes of the plurality of image attributes may be maximized in the second image.

Abstract: A request associated with reviewing content for a field of interest is received. A confidence is determined associated with the content including the field of interest. A machine learning (ML) model determines a first confidence associated with the content includes the field of interest. The field of interest is transmitted for review in instances where the first confidence is less than a confidence threshold. After review, an indication associated with a reviewer reviewing the content and the first confidence associated with the ML model identifying the field of interest is updated to a second confidence.

Abstract: Systems and methods for the annotation of source data in accordance with embodiments of the invention are disclosed. In one embodiment, a data annotation server system obtains a set of source data, provides at least one subset of source data to at least one annotator device, obtains a set of annotation data from the at least one annotator device for each subset of source data, classifies the source data based on the annotation data using a machine classifier for each subset of source data, generates annotator model data describing the characteristics of the at least one annotator device, and generates source data model data describing at least one piece of source data in the set of source data, where the source data model data includes label data identifying the estimated ground truth for each piece of source data in the set of source data.

Abstract: This disclosure describes automatically selecting and training one or more models for image recognition based upon training and testing (validation) data provided by a user. A service provider network includes a recognition service that may use models to process images and videos to recognize objects in the images and videos, features on the objects in the images and videos, and/or locate objects in the images and videos. The service provider network also includes a model selection and training service that may select one or more modeling techniques based on the objectives of the user and/or the amount of data provided by the user. Based on the selected modeling technique, the model selection and training service selects and trains one or more models for use by the recognition service to process images and videos using the training data. The trained model may be tested and validated using the testing data.

Abstract: Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The process may continue iteratively until the model converges. This enables a dataset to be labeled without requiring each item in the dataset to be individually and manually labeled by human labelers.

Abstract: Some embodiments of the invention provide a method for identifying geographic locations and for performing a fine-grained classification of elements detected in images captured from multiple different viewpoints or perspectives. In several embodiments, the method identifies the geographic locations by probabilistically combining predictions from the different viewpoints by warping their outputs to a common geographic coordinate frame. The method of certain embodiments performs the fine-grained classification based on image portions from several images associated with a particular geographic location, where the images are captured from different perspectives and/or zoom levels.

Abstract: Systems and methods for the annotation of source data in accordance with embodiments of the invention are disclosed. In one embodiment, a data annotation server system obtains a set of source data, provides at least one subset of source data to at least one annotator device, obtains a set of annotation data from the at least one annotator device for each subset of source data, classifies the source data based on the annotation data using a machine classifier for each subset of source data, generates annotator model data describing the characteristics of the at least one annotator device, and generates source data model data describing at least one piece of source data in the set of source data, where the source data model data includes label data identifying the estimated ground truth for each piece of source data in the set of source data.

Abstract: Systems and methods for the crowdsourced clustering of data items in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining categories for a set of source data includes obtaining a set of source data, determining a plurality of subsets of the source data, where a subset of the source data includes a plurality of pieces of source data in the set of source data, generating a set of pairwise annotations for the pieces of source data in each subset of source data, clustering the set of source data into related subsets of source data based on the sets of pairwise labels for each subset of source data, and identifying a category for each related subset of source data based on the clusterings of source data and the source data metadata for the pieces of source data in the group of source data.

Abstract: Systems and methods for performing behavioral detection using three-dimensional tracking and machine learning in accordance with various embodiments of the invention are disclosed. One embodiment of the invention involves a the classification application that directs a microprocessor to: identify at least a primary subject interacting with a secondary subject within a sequence of frames of image data including depth information; determine poses of the subjects; extract a set of parameters describing the poses and movement of at least the primary and secondary subjects; and detect a social behavior performed by at least the primary subject and involving at least the second subject using a classifier trained to discriminate between a plurality of social behaviors based upon the set of parameters describing poses and movement.

Abstract: Systems and methods for distributed data annotation in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a distributed data annotation server system includes a storage device configured to store source data, one or more annotators, annotation tasks and a processor, wherein a distributed data annotation application configures the processor to receive source data including one or more pieces of source data, select one or more annotators, create one or more annotation tasks for the selected annotators and source data, request one or more annotations for the source data using the annotation tasks, receive annotations, determine source data metadata for at least one piece of source data using the received annotations, generate annotator metadata for at least one annotator using the received annotations and the source data, and estimate the ground truth for the source data using the source data metadata and the annotator metadata.

Abstract: Systems and methods for determining annotator performance in the distributed annotation of source data in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for clustering annotators includes obtaining a set of source data, determining a training data set representative of the set of source data, obtaining sets of annotations from a set of annotators for a portion of the training data set, for each annotator determining annotator recall metadata based on the set of annotations provided by the annotator for the training data set and determining annotator precision metadata based on the set of annotations provided by the annotator for the training data set, and grouping the annotators into annotator groups based on the annotator recall metadata and the annotator precision metadata.

Abstract: Some embodiments of the invention provide a method for identifying geographic locations and for performing a fine-grained classification of elements detected in images captured from multiple different viewpoints or perspectives. In several embodiments, the method identifies the geographic locations by probabilistically combining predictions from the different viewpoints by warping their outputs to a common geographic coordinate frame. The method of certain embodiments performs the fine-grained classification based on image portions from several images associated with a particular geographic location, where the images are captured from different perspectives and/or zoom levels.

Abstract: Images are searched to locate faces that are the same as a query face. Images that include a face that is the same as the query face may be presented to a user as search result images. Images also may be sorted by the faces included in the images and presented to the user as sorted search result images. The user may provide explicit or implicit feedback regarding the search result images. Additional feedback may be inferred regarding the search result images based on the user-provided feedback, and the results may be updated based on the user-provided and inferred feedback.

Abstract: Systems and methods for the annotation of source data using confidence labels in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining confidence labels for crowdsourced annotations includes obtaining a set of source data, obtaining a set of training data representative of the set of source data, determining the ground truth for each piece of training data, obtaining a set of training data annotations including a confidence label, measuring annotator accuracy data for at least one piece of training data, and automatically generating a set of confidence labels for the set of unlabeled data based on the measured annotator accuracy data and the set of annotator labels used.

Abstract: Systems and methods for performing behavioral detection using three-dimensional tracking and machine learning in accordance with various embodiments of the invention are disclosed. One embodiment of the invention involves a the classification application that directs a microprocessor to: identify at least a primary subject interacting with a secondary subject within a sequence of frames of image data including depth information; determine poses of the subjects; extract a set of parameters describing the poses and movement of at least the primary and secondary subjects; and detect a social behavior performed by at least the primary subject and involving at least the second subject using a classifier trained to discriminate between a plurality of social behaviors based upon the set of parameters describing poses and movement.

Abstract: Systems and methods for performing behavioral detection using three-dimensional tracking and machine learning in accordance with various embodiments of the invention are disclosed. One embodiment of the invention involves a the classification application that directs a microprocessor to: identify at least a primary subject interacting with a secondary subject within a sequence of frames of image data including depth information; determine poses of the subjects; extract a set of parameters describing the poses and movement of at least the primary and secondary subjects; and detect a social behavior performed by at least the primary subject and involving at least the second subject using a classifier trained to discriminate between a plurality of social behaviors based upon the set of parameters describing poses and movement.

Abstract: Systems and methods for the crowdsourced clustering of data items in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining categories for a set of source data includes obtaining a set of source data, determining a plurality of subsets of the source data, where a subset of the source data includes a plurality of pieces of source data in the set of source data, generating a set of pairwise annotations for the pieces of source data in each subset of source data, clustering the set of source data into related subsets of source data based on the sets of pairwise labels for each subset of source data, and identifying a category for each related subset of source data based on the clusterings of source data and the source data metadata for the pieces of source data in the group of source data.

Abstract: Systems and methods for determining annotator performance in the distributed annotation of source data in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for clustering annotators includes obtaining a set of source data, determining a training data set representative of the set of source data, obtaining sets of annotations from a set of annotators for a portion of the training data set, for each annotator determining annotator recall metadata based on the set of annotations provided by the annotator for the training data set and determining annotator precision metadata based on the set of annotations provided by the annotator for the training data set, and grouping the annotators into annotator groups based on the annotator recall metadata and the annotator precision metadata.

Abstract: Systems and methods for the annotation of source data using confidence labels in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining confidence labels for crowdsourced annotations includes obtaining a set of source data, obtaining a set of training data representative of the set of source data, determining the ground truth for each piece of training data, obtaining a set of training data annotations including a confidence label, measuring annotator accuracy data for at least one piece of training data, and automatically generating a set of confidence labels for the set of unlabeled data based on the measured annotator accuracy data and the set of annotator labels used.

Abstract: Systems and methods for the annotation of source data using confidence labels in accordance embodiments of the invention are disclosed. In one embodiment of the invention, a method for determining confidence labels for crowdsourced annotations includes obtaining a set of source data, obtaining a set of training data representative of the set of source data, determining the ground truth for each piece of training data, obtaining a set of training data annotations including a confidence label, measuring annotator accuracy data for at least one piece of training data, and automatically generating a set of confidence labels for the set of unlabeled data based on the measured annotator accuracy data and the set of annotator labels used.