Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on analysis of the images, first and second estimated positions of the target vehicle within the time period; determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on analysis of the images, first and second estimated positions of the target vehicle within the time period; determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

A system provides intelligent gallery management for biometrics. A first gallery is obtained that includes biometric and/or other information on a population of people. An application is identified. A subset of the population of people is identified based on the application. A second gallery is derived from the first gallery by pulling the information for the subset of the population of people without pulling the information for the population of people not in the subset. Biometric identification (such as facial recognition) for the application may then be performed using the second gallery rather than the first gallery. In this way, the system is improved as less time is required for biometric identification, fewer device resources are used, and so on.

A training data generating system includes a processor. The processor acquires a plurality of medical images. The processor associates medical images with each other which are included in the plurality of medical images based on similarities of an imaging target to generate an associated image group including medical images associated with each other. The processor outputs, to a display, an application target image to be an image as an application target of representative training information based on the associated image group. The processor accepts input of representative contour information indicative of a contour of a specific region in the application target image as the representative training information. The processor applies contour information, as training information, to each medical image included in the associated image group based on the representative training information input to the application target image.

A training data generating system includes a processor. The processor acquires a plurality of medical images. The processor associates medical images with each other which are included in the plurality of medical images based on similarities of an imaging target to generate an associated image group including medical images associated with each other. The processor outputs, to a display, an application target image to be an image as an application target of representative training information based on the associated image group. The processor accepts input of representative contour information indicative of a contour of a specific region in the application target image as the representative training information. The processor applies contour information, as training information, to each medical image included in the associated image group based on the representative training information input to the application target image.

There are provided a learning apparatus, a learning method, and a program that enable, by using one type of device data, learning of a plurality of models using different data formats. A learning data acquiring section (36) acquires first data that is first-type device data. A first learning section (42) performs learning of a first model (34(1)) in which an estimation using the first-type device data is executed by using the first data. A learning data generating section (40) generates second data that is second-type device data the format of which differs from the format of the first-type device data on the basis of the first data. A second learning section (44) performs learning of a second model (34(2)) in which an estimation using the second-type device data is executed by using the second data.

Systems, devices, methods, and computer-readable media for evaluation and visualization of machine learning data drift. A method can include receiving a series of data indicating accuracy and confidence associated with classification of respective batches of input samples, and dynamically displaying, on the GUI, a concurrent plot of the accuracy and confidence as the series of data are received.

A method includes obtaining an image of a scene and identifying one or more labels for one or more objects captured in the image. The method also includes generating one or more domain-specific augmented images by modifying the image, where the one or more domain-specific augmented images are associated with the one or more labels. In addition, the method includes training or retraining a machine learning model using the one or more domain-specific augmented images and the one or more labels. Generating the one or more domain-specific augmented images may include at least one of modifying the image to include a different amount of motion blur, modifying the image to include a different lighting condition, and modifying the image to include a different weather condition.

A system for inspecting the reliability of an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: obtaining a plurality of images; categorizing the images into a plurality of image classes; calculating a plurality of probability outcomes; determining whether highest predicted probabilities of the images are less than a first threshold and whether an entropy of a predicted density of the probability outcomes exceeds a second threshold; indicating whether the image is associated with the image classes; ranking, the image amongst the plurality of images; filtering, a plurality of low reliability images according to a third threshold; providing, a likelihood of whether a user scanned a vehicle object associated with the image; and identifying a percentage of user scan failures.

A recognition processor calculates a recognition score indicating a possibility at which a predetermined target object is included in a partial region of a captured picture. In a case where a picture size of the partial region is smaller than a threshold value, the recognition processor calculates a recognition score using first recognition dictionary data generated by machine learning that sets a picture having a picture size smaller than a predetermined value as an input picture. In a case where a picture size of the partial region is larger than or equal to the threshold value, the recognition processor calculates a recognition score using second recognition dictionary data generated by machine learning that sets a picture having a picture size larger than or equal to the predetermined value as an input picture.