Abstract: An example method of classifying user content based on a set of standards is described herein. The method includes receiving the user content, generating numeric representations of the user content, and identifying one or more standards of the set of standards applying to the user content based on a comparison of the numeric representation of the user content and numeric representations of the one or more standards.

Abstract: An item generation interface may generate knowledge assessment items directed a subject area based on a set of model items collectively directed to the subject area. The item generation interface may group the set of model assessment items into a plurality of similar item groups using numeric features corresponding to the model assessment items. Similar item groups may include model assessment items covering conceptually similar concepts within the subject area. A conditioning input may be generated for each of the item groups based on the numeric features corresponding to the model assessment items in the item group. Responsive to providing the conditioning inputs to a transformer-based natural language generation model, the item generation interface may receive raw assessment items from the transformer-based natural language generation model. Knowledge assessment items may be identified from the raw assessment items.

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models based at least in part on the set of p-scores and the set of n-scores.

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.