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 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 employing inference models based on available processing resources are provided. For example, available processing resources information may be received, inference model may be selected (for example, based on the received information), and the selected inference model may be utilized. In some cases, an update to the available processing resources information may be received, the selected inference model may be updated (for example, based on the received update), and the updated inference model may be utilized.

Abstract: Systems and methods for estimating the required processing resources for machine learning tasks are provided. For example, properties of a machine learning training task may be obtained, properties of external devices may be obtained, and the processing resources requirements of the machine learning training task may be estimated (for example using the properties of the external devices). In some examples, the estimation may be provided to a user. In some examples, a group of devices may be selected, and the selected group of devices may be triggered to perform the machine learning training task.

Abstract: Systems and methods for generating and using descriptors of artificial neural networks are provided. For example, an artificial neural network may be obtained, the artificial neural network may be obtained, descriptors of the segments may be calculated, and a descriptor of the artificial neural network may be compiled. In some examples, a match score for a pair of artificial neural networks may be calculated (for example using the descriptors compiled for the two artificial neural networks), and actions may be selected based on the matching score.

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 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 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 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 maintaining a project schedule in a dataset management system are provided. For example, a progress update and a project schedule record may be obtained. The project schedule record may be updated, for example according to the progress update. In some examples, a delay may be predicted, and a corresponding indication may be provided.

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 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: System and method for image processing are provided. Image based information may be obtained. The image based information may be analyzed to identify instances of a repeated activity of a selected person. Properties of the repeated activity of the selected person may be determined based on the identified instances of the repeated activity.

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 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 selected based on the scene information. Further images may be analyzed using the selected 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.

Abstract: System and method for image classification based on Tsallis entropy are provided. An image may be segmented to obtain image regions. The image regions may be analyzed to generate histograms. The histograms may be analyzed to compute Tsallis entropy values. The image regions may be associated with categories based on the Tsallis entropy values. Image regions associated with a first category may be classified to obtain classification information. The classification information may be transmitted to an external device. Information related to image regions associated with a second category may be transmitted to the external device. The external device may be configured to classify the image regions associated with the second category based on the transmitted information.

Abstract: System and method for generating inference models are provided. Scene information may be received. Inference models may be generated based on the scene information. At least one of the generated inference models may be transmitted. A result of applying input data to the transmitted inference model may be received. Input data may be received. A result of applying the received input data to at least one of the generated inference models may be generated. A notification may be provided based on the received result and the generated result.

Abstract: Systems and methods for updating inference models are provided. Information based on a result of applying input data to an inference model may be received. An update to the inference model may be generated based on the received information. The generated update may be transmitted.