Methods and systems for out-of-distribution (OOD) detection in autonomous driving systems are described. A method for use in an autonomous driving system may include filtering feature vectors. The feature vectors may be filtered using a first filter to obtain clusters of feature vectors. The method may include assigning one or more images to a respective cluster based on a feature vector of the image. The method may include filtering a subset of the images using a second filter to determine a classification model. The method may include storing the classification model on a vehicle control system of a vehicle. The method may include detecting an image using a vehicle sensor. The method may include classifying the detected image based on the classification model. The method may include performing a vehicle action based on the classified detected image.

A method including bit-operation and sub-code/substring filtering for image searching using a full-text search engine. The method can include determining a first binary vector comprising first binary substrings for a first image. The method also can include obtaining a respective second binary vector comprising second binary substrings for each of second images from a database. The method additionally can include determining a respective substring distance for each of the binary substring for each of the second images. The respective substring distance can be between at least a pair of a first binary substring of the first binary substrings of the first binary vector and a respective corresponding second binary substring of the second binary substrings of the respective second binary vector for each of the second images. In some embodiments, the method further can include after determining the respective substring distance for each of the binary substring for each of the second images, when the respective substring distance for one or more of the second images is not greater than a predetermined substring distance threshold, including the one or more of the second images in a search result. The method also can include determining a respective image distance for each respective third image of the search result, the respective image distance being between the first image and the each respective third image of the search result. The method additionally can include after determining the respective image distance for the each respective third image of the search result, when the respective image distance is greater than the predetermined image distance threshold, culling the each respective third image from the search result. Other embodiments are disclosed.

A method including bit-operation and sub-code/substring filtering for image searching using a full-text search engine. The method can include determining a first binary vector comprising first binary substrings for a first image. The method also can include obtaining a respective second binary vector comprising second binary substrings for each of second images from a database. The method additionally can include determining a respective substring distance for each of the binary substring for each of the second images. The respective substring distance can be between at least a pair of a first binary substring of the first binary substrings of the first binary vector and a respective corresponding second binary substring of the second binary substrings of the respective second binary vector for each of the second images. In some embodiments, the method further can include after determining the respective substring distance for each of the binary substring for each of the second images, when the respective substring distance for one or more of the second images is not greater than a predetermined substring distance threshold, including the one or more of the second images in a search result. The method also can include determining a respective image distance for each respective third image of the search result, the respective image distance being between the first image and the each respective third image of the search result. The method additionally can include after determining the respective image distance for the each respective third image of the search result, when the respective image distance is greater than the predetermined image distance threshold, culling the each respective third image from the search result. Other embodiments are disclosed.

Described is a system and method for enabling visual search for information. With each selection of an object included in an image, additional images that include visually similar objects are determined and presented to the user.

Described is a system and method for enabling visual search for information. With each selection of an object included in an image, additional images that include visually similar objects are determined and presented to the user.

A processor-implemented method with input data classification includes: extracting an input embedding vector including a feature of biometric information of a user from input data including the biometric information; determining an adaptive embedding vector adaptive to the input embedding vector, based on a combination of a plurality of enrollment embedding vectors that are based on enrollment data; and classifying the input data based on a similarity between the input embedding vector and the adaptive embedding vector.

A processor-implemented method with input data classification includes: extracting an input embedding vector including a feature of biometric information of a user from input data including the biometric information; determining an adaptive embedding vector adaptive to the input embedding vector, based on a combination of a plurality of enrollment embedding vectors that are based on enrollment data; and classifying the input data based on a similarity between the input embedding vector and the adaptive embedding vector.

A system and method for flight chart element searching is disclosed. A host computing device is configured to convert PDF flight chart files to SVG flight chart files defined in XML, detect flight chart elements in the SVG flight chart files, convert the SVG flight chart files to flight charts defined in aircraft display hardware directives, and combine the flight charts and flight chart element data into a flight chart database. An aircraft computing device is configured to display a flight chart and a GUI using an aircraft display, and highlight a flight chart element in response to a user searching for and selecting a flight chart element name.

The present disclosure generates a digital twin of the interior of a vehicle to initiate and track maintenance issues. In one aspect, the digital twin is formed using multiple captured images of the interior of the vehicle where multiple components in those images are identified using a machine learning (ML) model. The components identified by the ML model are then mapped to a model (e.g., a 3D model) of the components that lists their location in the vehicle and an identifier (e.g., a part number or serial number). In this manner, the digital twin can identify, using the identifiers, the various components in the images captured by a camera.

Systems and methods for performing image search are described. An image search method may include generating a feature vector for each of a plurality of stored images using a machine learning model trained using a rotation loss term, receiving a search query comprising a search image with object having an orientation, generating a query feature vector for the search image using the machine learning model, wherein the query feature vector is based at least in part on the orientation, comparing the query feature vector to the feature vector for each of the plurality of stored images, and selecting at least one stored image of the plurality of stored images based on the comparison, wherein the at least one stored image comprises a similar orientation to the orientation of the object in the search image.