There is provided a method, comprising: providing a training dataset including, medical images and corresponding text based reports, and concurrently training a natural language processing (NLP) machine learning (ML) model for generating a NLP category for a target text based report and a visual ML model for generating a visual finding for a target image, by: training the NLP ML model using the text based reports of the training dataset and a ground truth comprising the visual finding generated by the visual ML model in response to an input of the images corresponding to the text based reports of the training dataset, and training the visual ML model using the images of the training dataset and a ground truth comprising the NLP category generated by the NLP ML model in response to an input of the text based reports corresponding to the images of the training dataset.

To reduce the reliance on software for complex computations used in machine sensory perception, a sensory perception accelerator may include a neural network accelerator a linear algebra accelerator. The neural network accelerator may include systolic arrays to perform neural network computation circuits concurrently on image data and audio data. The linear algebra accelerator may include matrix computation circuits operable to perform matrix operations on image data and motion data.

A sensor transformation attention network (STAN) model including sensors, attention modules, a merge module and a task-specific module is provided. The attention modules calculate attention scores of feature vectors corresponding to the input signals collected by the sensors. The merge module calculates attention values of the attention scores, and generates a merged transformation vector based on the attention values and the feature vectors. The task-specific module classifies the merged transformation vector.

Example implementations involve a location system, which can involve associating each location of one or more unidentified targets detected from sensor data of the one or more sensors with identifiers corresponding to the transmitter of each of the one or more pairs of electronic devices, by calculating first distance relationships indicative of relationships of distances between the each location of the one or more unidentified targets detected from the sensor data of the one or more sensors and a reference point; calculating second distance relationships indicative of relationships of distances between the transmitter and the receiver of each of the one or more pairs of electronic devices; and associating the identifiers corresponding to the transmitter of the each of the one or more pairs of electronic devices with the each location of the one or more unidentified targets based on the first distance relationships and the second distance relationships.

Methods, systems, and programming for user identification are presented. In one example, a system for acquiring biometric information is disclosed. The system comprises a housing including a surface for a person to place a finger thereon. The system also comprises a sensor, a first image acquisition portion, and a second image acquisition portion. The sensor is configured for sensing presence of the finger when the person places the finger on the surface. The first image acquisition portion is configured for acquiring a fingerprint image of the finger placed on the surface. The second image acquisition portion is configured for acquiring a finger vein image of the finger placed on the surface. The first and second image acquisition portions acquire their respective images at different times.

Disclosed are systems and methods for determining whether an unknown product matches a scanned barcode during checkout. The system includes a checkout lane having a flatbed scanning area with scanning devices and a point of sale (POS) terminal that scans a product identifier of an unknown product, identifies a product associated with the scanned product identifier, and transmits, to a computing system, product information. An overhead camera idnentifies, based on detecting an optical signal from the POS terminal, that a scanning event occurred, captures image data of the unknown product, and transmits, to the computing system, the image data. The computing system generates machine learning product identification models for identifying unknown products, identifies candidate product identifications for the unknown product based on applying the models to the image data, and determines, based on the candidate product identifications and the information about the product, whether the unknown product matches the product.

A system and method include training of an artificial neural network to generate an output data set, the training based on the plurality of sets of emission data acquired using a first imaging modality and respective ones of data sets acquired using a second imaging modality.

The present disclosure provides a target detection and model training method and apparatus, a device and a storage medium, and relates to the field of artificial intelligence, and in particular, to computer vision and deep learning technologies, which may be applied to smart city and intelligent transportation scenarios. The target detection method includes: performing feature extraction processing on an image to obtain image features of a plurality of stages of the image; performing position coding processing on the image to obtain a position code of the image; obtaining detection results of the plurality of stages of a target in the image based on the image features of the plurality of stages and the position code; and obtaining a target detection result based on the detection results of the plurality of stages.

A camera of a monitoring system detects electromagnetic radiation in at least two different spectrums. The system includes a database of stored images, where the stored images are stored in pairs of images corresponding to each spectrum and to a common face. A controller identifies a match from among the stored images by comparing the first image obtained by the camera in the first spectrum to each of the first images in the database and by comparing the second image obtained from the camera in the second spectrum to the corresponding second image stored in the database. A match is identified when the first image and the second image from the camera match the first image and the corresponding second image for a pair of images in the database.

Provided are methods for customized tags for annotating sensor data, which can include receiving sensor data captured during a plurality of sensor data capture sessions, processing the sensor data using a plurality of machine learning models to identify a plurality of capture session collections represented in the sensor data, filtering the sensor data based at least partly on a user-specified category of the plurality of categories of capture session to identify a capture session collection, of the plurality of capture session collections, representing sensor data of one or more sensor data capture sessions that conforms to the user-specified category, and transmitting the sensor data of one or more sensor data capture sessions that conforms to the user-specified category to an end user computing device. Systems and computer program products are also provided.