Abstract: Systems and methods for controlling access to a dataset management system using permission records are provided. For example, a request to access information in a dataset management system may be obtained from an entity, and a permission record associated with the entity may be selected. Further, it may be determined if the entity has permission to access the information. In some examples, when the entity has permission to access the information, the access to the information may be allowed. In some examples, when the entity has no permission to access the information, the access to the information may be denied.

Abstract: Systems and methods for class aware object marking are provided. For example, an indication of a selected object type of a plurality of alternative object types may be received. Further, in some examples, an image may be presented to a user. Further, in some examples, an input to an object selection tool may be received from the user. Further, in some examples, in response to a first selected object type and the input to the object selection tool, a first region of the image corresponding to an object of the first selected object type in the image may be selected, and in response to a second selected object type and the input to the object selection tool, a second region of the image corresponding to an object of the second selected object type in the image may be selected.

Abstract: Systems and methods for maintaining a project schedule in a dataset management system are provided. For example, a progress update comprising an indication of a status of training of a first machine learning algorithm may be received using at least one communication device from an external device involved in the training the first machine learning algorithm. A project schedule record may be accessed. An expected running time of the training of the first machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm. Information related to training of a second machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm.

Abstract: Systems and methods for presenting inference models based on interrelationships among inference models are provided. For example, information related to a group of at least three inference models may be obtained. Further, in some examples, a plurality of interrelationship records may be obtained, wherein each interrelationship record may correspond to one subgroup of the group of at least three inference models, and each subgroup may comprise at least two inference models. Further, in some examples, the plurality of interrelationship records may be used to determine information related to a first inference model of the group of at least three inference models. Further, in some examples, the determined information may be used to present the first inference model to a user.

Abstract: Systems and methods for enriching datasets while learning are provided. For example, intermediate results of training machine learning algorithms may be obtained. Additional training examples may be selected based on the intermediate results. In some cases, synthetic examples may be generated based on the intermediate results. The machine learning algorithms may be further trained using the selected additional training examples and/or the generated synthetic examples.

Abstract: Systems and methods for personalized quality assurance of inference models are provided. For example, data items associated with a group of devices may be obtained, results of applying the data items to inference models may be obtained, the results of applying the data items to a first inference model may be compared with the results of applying the data items to a second inference model, and the compatibility of the second inference model to the group of devices may be assessed, for example based on the comparison results. In some examples, when the second inference model is found compatible, the second inference model may be utilized in tasks associated with the group of devices. In some examples, when the second inference model is found incompatible, the system may forgo the usage of the second inference model in one or more tasks associated with the group of devices.

Abstract: Systems and methods for selective use of examples are provided. For example, information associated with external devices may be obtained, weights may be assigned to examples using the obtained information, and inference models may be generated using the assignment of weights. In some examples, update information associated with the external devices may be obtained, the assignment of weights may be updated using the update information, and updated inference models may be generated using the updated assignment of weights.

Abstract: Systems, methods and non-transitory computer readable media for image processing are provided. Images may be obtained, for example by capturing the images using an image sensor. The images may be analyzed to identify scene information. An inference model may be obtained based on the scene information. The scene information may be used to determine parameters for modifying the obtained inference model. Additional images may be obtained, for example using the image sensor. The additional images may be analyzed using the inference model.

Abstract: System and method for image processing are provided. A stream of images may be obtained, for example by capturing images using an image sensor. Points in time associated with an activity may be obtained. For each point in time, the stream of images may be analyzed to identify events related to the activity and preceding the point in time. Based on the identified events, an event detection rule configured to analyze images to detect at least one event may be obtained. Image data may be obtained, and the image data may be analyzed using the event detection rule to detect events matching selected criteria in the image data.

Abstract: Systems and methods for maintaining a project schedule in a dataset management system are provided. For example, a progress update comprising an indication of a status of training of a first machine learning algorithm may be received using at least one communication device from an external device involved in the training the first machine learning algorithm. A project schedule record may be accessed. An expected running time of the training of the first machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm. Information related to training of a second machine learning algorithm may be updated in the project schedule record based on the status of the training of the first machine learning algorithm.

Abstract: Systems and methods for causing actions in a dataset management system are provided. For example, a change to data maintained by the dataset management system may be detected, and a rule associated with the data and with an action may be obtained. Further, it may be determined if the change is a trigger according to the rule. In some examples, when the change is determined to be a trigger according to the rule, the action associated with the rule may be performed, for example with the data associated with the change. In some examples, when the change is determined not to be a trigger according to the rule, the system may forgo the performance of the action, for example for the data associated with the change.

Abstract: Systems and methods for merging annotations of datasets are provided. For example, assignments of labels to data-points may be obtained, confidence levels associated with the assignments of labels may be obtained. Further, the assignments of labels may be merged, for example based on the confidence levels. In some cases, inference models may be generated using the merged assignment of labels. In some examples, an update to the assignments of labels to data-points and/or the confidence levels may be obtained, and the merged assignment of labels may be updated.

Abstract: Systems and methods for determining a global update to inference models using update information from a plurality of external systems are provided. A first and a second update information associated with an inference model may be received. The first update information may be based on an analysis of a first plurality of training examples by a first external system, and the second update information may be based on an analysis of a second plurality of training examples accessible by a second external system. Information related to the distribution of the first plurality of training examples and the second plurality of training examples may be obtained. The first and the second update information, and the information related to the distribution of the first plurality of training examples and the second plurality of training examples may be used to determine a global update to the inference model.

Abstract: System and method for image processing are provided. A stream of images may be obtained, for example by capturing images using an image sensor. Points in time associated with an activity may be obtained. For each point in time, the stream of images may be analyzed to identify events related to the activity and preceding the point in time. Based on the identified events, an event detection rule configured to analyze images to detect at least one event may be obtained. Image data may be obtained, and the image data may be analyzed using the event detection rule to detect events matching selected criteria in the image data.

Abstract: System and method for image processing are provided. Images may be obtained, for example by capturing the images using an image sensor. The images may be analyzed to identify objects. Based on the identified objects, regions in current images and/or in images captured in the future may be identified. Processing schemes for the identified regions may be selected, for example based on the identified objects. The regions may be processed according to the selected processing schemes.

Abstract: System and method for visual recognition are provided. Image data may be captured. First descriptive information may be received, for example from an external device. The image data may be analyzed to identify a group of items matching the first descriptive information. In case the group of items comprises at least two items, a second descriptive information matching a subset of the identified group of items may be generated, and in some cases the second descriptive information may be provided to the external device. In case the group of items is a group of a single item, an association of the first descriptive information and an identification value may be stored in memory, and in some cases a corresponding indication may be provided to the external device. In case the group of items is an empty group, a corresponding indication may be provided to the external device.

Abstract: Systems and methods for controlling access to a dataset management system using quotas are provided. For example, a request to perform an action in a dataset management system may be obtained from an entity, and a quota record associated with the entity may be selected. Further, it may be determined if the entity has sufficient quota to perform the action. In some examples, when the entity has sufficient quota to perform the action, the request may be allowed. In some examples, when the entity has insufficient quota to perform the action, the request may be denied.

Abstract: Systems and methods for incremental annotation of datasets are provided. For example, a group of labeled examples and a group of unlabeled examples may be obtained, a first inference model may be generated using the group of labeled examples, labels may be assigned to at least part of the group of unlabeled examples using the first inference model, confidence levels may be assigned to the assigned labels, a subset of the group of unlabeled examples may be selected using the confidence levels, and in some cases a second inference model may be generated using the selected subset and/or the corresponding assigned labels.

Abstract: Systems and methods for controlling access to a dataset management system using quotas are provided. For example, a request to perform an action in a dataset management system may be obtained from an entity, and a quota record associated with the entity may be selected. Further, it may be determined if the entity has sufficient quota to perform the action. In some examples, when the entity has sufficient quota to perform the action, the request may be allowed. In some examples, when the entity has insufficient quota to perform the action, the request may be denied.

Abstract: Systems and methods for personalized selection of inference models based on usage of other inference models are provided. For example, an indication of a usage of a first inference model by a first personalized device and an indication of a usage of a second inference model by a second personalized device may be received. Further, a first additional inference model may be selected based on the first inference model and a second additional inference model may be selected based on the second inference model. Further, information related to the selected first additional inference model may be provided to the first personalized device, and information related to the selected second additional inference model may be provided to the second personalized device.