A human-object interaction detection method, a neural network and a training method therefor is provided. The human-object interaction detection method includes: performing first target feature extraction on an image feature of an image; performing first interaction feature extraction on the image feature; processing a plurality of first target features to obtain target information of a plurality of detected targets; processing one or more first interaction features to obtain motion information of a motion, human information of a human target corresponding to each motion, and object information of an object target corresponding to each motion; matching the plurality of detected targets with one or more motions; and updating human information of a corresponding human target based on target information of a detected target matching the corresponding human target, and updating object information of a corresponding object target based on target information of a detected target matching the corresponding object target.

A human-object interaction detection method, a neural network and a training method therefor is provided. The human-object interaction detection method includes: performing first target feature extraction on image features of an image to obtain first target features; performing first interaction feature extraction on image features to obtain first interaction features and scores thereof; determining at least some first interaction features in the first interaction features based on the score of each of the first interaction features; determining first motion features based on the at least some first interaction features and the image features; processing the first target features to obtain target information of targets in the image; processing the first motion features to obtain motion information of one or more motions in the image; and matching the targets with the motions to obtain a human-object interaction detection result.

Systems and methods for automatically analyzing spectral power measurements to identify abnormalities. The systems and methods may receive measurements comprising RF power measured over a contiguous range of frequencies, where at least a first portion of the contiguous range is used to transmit signals and at least a second portion of the contiguous range is unused. Respective boundaries of the unused portions may be identified and infilled to provide modified measurements. The modified measurements may be automatically analyzed to identify the abnormalities.

Machine learning techniques are used, in one embodiment, to mitigate multipath in an L5 GNSS receiver. In one embodiment, training data is generated to provide ground truth data for excess path length (EPL) corrections for a set of received GNSS signals. A system extracts features from the set of received GNSS signals and uses the extracted features and the ground truth data to train a set of one or more neural networks that can produce EPL corrections for pseudorange measurements. The trained set of one or more neural networks can be deployed in GNSS receivers and used in the GNSS receivers to correct pseudorange measurements using EPL corrections provided by the trained set of neural networks.

A system for determining relevance of a light source to an automobile includes at least one camera adapted to capture images of light sources in proximity to the automobile, a controller in communication with the at least one camera and adapted to receive captured images from the at least one camera, the controller further adapted to estimate an orientation of at least one light source relative to the automobile, classify the at least one light source as one of relevant and irrelevant, and, when the at least one light source is classified as relevant, send information about the at least one light source to a planning module for the automobile.

An audio sample including speech and ambient sounds is transmitted to a vehicle computer. Recorded audio is received from the vehicle computer, the recorded audio including the audio sample broadcast by the vehicle computer and recorded by the vehicle computer and recognized speech from the recorded audio. The recognized speech and text of the speech are input to a machine learning program that outputs whether the recognized speech matches the text. When the output from the machine learning program indicates that the recognized speech does not match the text, the recognized speech and the text are included in a training dataset for the machine learning program.

Apparatuses, systems, and techniques to detect loops in neural network graphs. In at least one embodiment, one or more loops are detected within one or more graphs corresponding to one or more neural networks.

The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating topic divergence classifications for digital videos based on words from the digital videos and further based on a digital text corpus representing a target topic. Particularly, the disclosed systems utilize a topic-specific knowledge encoder neural network to generate a topic divergence classification for a digital video to indicate whether or not the digital video diverges from a target topic. In some embodiments, the disclosed systems determine topic divergence classifications contemporaneously in real time for livestream digital videos or for stored digital videos (e.g., digital video tutorials). For instance, to generate a topic divergence classification, the disclosed systems generate and compare contextualized feature vectors from digital videos with corpus embeddings from a digital text corpus representing a target topic utilizing a topic-specific knowledge encoder neural network.

A method is provided. The method includes obtaining an enhanced state graph. The enhanced state graph represents a set of objects within an environment and a set of positions of the set of objects. The enhanced state graph includes a set of object nodes, a set of property nodes and a set of goal nodes to represent a set of objectives. The method also includes generating a set of instructions for a set of mechanical systems based on the enhanced state graph. The set of mechanical systems is configured to interact with one or more of the set of objects within the environment. The method further includes operating the set of mechanical systems to achieve the set of objectives based on the set of instructions.

Methods and systems are provided for detecting and responding to dangerous road users. In some aspects, a process can include steps for receiving sensor data of a detected object from an autonomous vehicle, determining whether the detected object is exhibiting a dangerous attribute, generating output data based on the determining of whether the detected object is exhibiting the dangerous attribute, and updating a machine learning model based on the output data relating to the dangerous attribute.