A computer implemented method determines differences between documents. The method includes parsing a first document and a second document into respective distinct instances of content. The distinct instances of content are classified into different categories. Category specific matching algorithms are applied to each of the respective instances of content to determine a similarity score for each of the respective instances of content. Semantic differences between the first document and the second document are analyzed as a function of the similarity scores. A characterization of the semantic differences is generated.

A method of balancing a dataset for a machine learning model includes identifying confusing classes of few-shot classes for a machine learning model during validation. One of the confusing classes and an image from one of the few-shot classes are selected. An image perturbation is computed such that the selected image is classified as the selected confusing class. The selected image is modified with the computed perturbation. The modified selected image is added to a batch for training the machine learning model.

Provided is a method, computer program product, and system for predicting image sharing decisions using machine learning. A processor may receive a set of annotated images and an associated text input from each user of a plurality of users. The processor may train, using the set of annotated images and the associated text input from each user, a neural network model to output an image sharing decision that is specific to a user.

A system for refining an item identification model detects a triggering event at a platform, where the triggering event corresponds to a user placing the item on a platform. The system captures images of the item. The system extracts a set of features from at least one of the images. The system identifies the item based on the set of features. The system receives an indication that the item is not identified correctly. The system receives an identifier of the item. The system identifies the item based on the identifier of the item. The system feeds the identifier of the item and the images to the item identification model. The system retrains the item identification model to learn to associate the item to the images. The system updates the set of features based on the determined association between the item and the images.

A system for updating a training dataset of an item identification model determines that an item is not included in a training dataset. In response to determining that the item is not included in the training dataset, the system obtains an identifier of the item. The system detects a triggering event at a platform, where the triggering event corresponds to a user placing the item on a platform. The system captures images of the item. The system extracts a set of features associated with the item from the images. The system associates the item to the identifier and the set of features. The system adds a new entry to the training dataset, where the new entry represents the item labeled with the identifier and the set of features.

A device configured to receive a first point cloud data for a first item, to identify a first plurality of data points for the first object within the first point cloud data, and to extract the first plurality of data points from the first point cloud data. The device is further configured to receive a second point cloud data for the first item, to identify a second plurality of data points for the first object within the second point cloud data, and to extract a second plurality of data points from the second point cloud data. The device is further configured to merge the first plurality of data points and the second plurality of data points to generate combined point cloud data and to determine dimensions for the first object based on the combined point cloud data.

A system for identifying items based on aggregated metadata obtains images of an item. The system extracts a set of features from images of the item. The system identifies a first value of a first feature associated with a first image of the item. The system identifies a second value of the first feature associated with a second image of the item. The system aggregates the first value and the second value. The system associates the item to the aggregated first value and the second value, where the aggregated first value and the second value represent the first feature of the item. The system adds a new entry for each image of the item to a training dataset associated with an item identification model.

An adaptive processing method for new scenes in autonomous driving, comprising: obtaining scene data corresponding to new scene of vehicle driving, wherein the scene data describes vehicles state and driving operations in the new scene; obtaining a test set of the new scene based on processing the scene data by a preset distribution; updating parameters of a pre-training model by inputting the test set, and obtaining a scene model adapted to the new scene based on gradient iteration of general model parameters of the pre-training model, wherein the scene model is configured to output an autonomous driving strategy for the vehicle in the new scene. Therefore, the autonomous driving vehicle transforms a new scene to a known scene, and no longer be troubled by unpredictable new scenes, and greatly enhance the reliability and stability of autonomous driving.

A system for capturing images for training an item identification model obtains an identifier of an item. The system detects a triggering event at a platform, where the triggering event corresponds to a user placing the item on a platform. The system causes the platform to rotate. The system causes at least one camera to capture an image of the item while the platform is rotating. The system extracts a set of features associated with the item from the image. The system associates the item to the identifier and the set of features. The system adds a new entry to a training dataset of the item identification model, where the new entry represents the item labeled with the identifier and the set of features.

A device configured to receive a first encoded vector and receive one or more feature descriptors for a first object. The device is further configured to remove one or more encoded vectors from an encoded vector library that are not associated with the one or more feature descriptors and to identify a second encoded vector in the encoded vector library that most closely matches the first encoded vector based on the numerical values within the first encoded vector. The device is further configured to identify a first item identifier in the encoded vector library that is associated with the second encoded vector and to output the first item identifier.