The present subject matter provides various technical solutions to technical problems facing advanced driver assistance systems (ADAS) and autonomous vehicle (AV) systems. In particular, disclosed embodiments provide systems and methods that may use cameras and other sensors to detect objects and events and identify them as predefined signal classifiers, such as detecting and identifying a red stoplight. These signal classifiers are used within ADAS and AV systems to control the vehicle or alert a vehicle operator based on the type of signal. These ADAS and AV systems may provide full vehicle operation without requiring human input. The embodiments disclosed herein provide systems and methods that can be used as part of or in combination with ADAS and AV systems.

An artificial intelligence apparatus for recognizing an object included in image data can include a camera, a communication modem, a memory configured to store an image recognition model, a natural language processing (NLP) model, and an NLP model-based image recognition model learned based on the NLP model, and a processor is configured to receive image data from the camera or the communication modem, in response to recognizing an object included in the image data using the image recognition model, generate first recognition information on the object included in the image data, and in response to the recognizing the object included in the image data using the image recognition model being unsuccessful, generate second recognition information on the object included in the image data based on recognizing the object using the NLP model-based image recognition model.

Automatic fact extraction that involves tokenizing text in unstructured information to generate a token list. Parent entity rules defined for a selected domain are applied to the token list to identify a parent entity. Related entity rules that are defined for a related entity linked to the parent entity are applied to the token list to identify the related entity. The related entity is added as an extracted fact of the parent entity to a fact list. The extracted fact is transmitted as structured information to a repository.

A property vector representing extractable measurable properties, such as musical properties, of a file is mapped to semantic properties for the file. This is achieved by using artificial neural networks “ANNs” in which weights and biases are trained to align a distance dissimilarity measure in property space for pairwise comparative files back towards a corresponding semantic distance dissimilarity measure in semantic space for those same files. The result is that, once optimised, the ANNs can process any file, parsed with those properties, to identify other files sharing common traits reflective of emotional-perception, thereby rendering a more liable and true-to-life result of similarity/dissimilarity. This contrasts with simply training a neural network to consider extractable measurable properties that, in isolation, do not provide a reliable contextual relationship into the real-world.

The charged particle beam apparatus includes a charged particle beam optical system that irradiates a sample mounted on a sample stage with a charged particle beam; a detector that detects a signal generated from the sample; a charged particle beam imaging device that acquires an observation image from the signal detected by the detector; an optical imaging device that captures an optical image of the sample; a stage that rotatably holds the sample stage; a stage control device that controls movement and rotation of the stage; and an image composition unit that combines the plurality of optical images to generate a composite image. The stage control device is configured to move the stage so that the center of an imaging range of the optical imaging device is located at a position different from the rotation center of the stage and then, to rotate the stage, the optical imaging device acquires a plurality of optical images relating to different positions of the sample by rotation operation, and the image composition unit combines the plurality of optical images obtained by the rotation operation to generate a composite image.

A method for generating a tag of a video, an electronic device, and a storage medium are related to a field of natural language processing and deep learning technologies. The detailed implementing solution includes: obtaining multiple candidate tags and video information of the video; determining first correlation information between the video information and each of the multiple candidate tags; sorting the multiple candidate tags based on the first correlation information to obtain a sort result; and generating the tag of the video based on the sort result.

Embodiments of the present disclosure disclose an image processing method and apparatus. The method may include obtaining a harmonized image. The harmonized image may be harmonized with a promotional content image. The method may further include performing context feature extraction on the harmonized image to obtain context feature information of the harmonized image and extracting multi-level semantic information of an object in the harmonized image based on the context feature information. The method may further include performing image reconstruction based on the context feature information and the multi-level semantic information to obtain a reconstructed image. This solution can improve an image harmonization effect.

An intelligent assistant records speech spoken by a first user and determines a self-selection score for the first user. The intelligent assistant sends the self-selection score to another intelligent assistant, and receives a remote-selection score for the first user from the other intelligent assistant. The intelligent assistant compares the self-selection score to the remote-selection score. If the self-selection score is greater than the remote-selection score, the intelligent assistant responds to the first user and blocks subsequent responses to all other users until a disengagement metric of the first user exceeds a blocking threshold. If the self-selection score is less than the remote-selection score, the intelligent assistant does not respond to the first user.

A computing system for learning a device type and message formats used by a device is provided. The computing system includes an interface and a processor. The interface is receptive of documents describing identification information and communication and application protocols of devices. The processor is coupled with the interface to obtain rules of network packet analysis using document analytics and identify identification information and communication and application protocols of network messages from devices using the rules.

A surveillance system is coupled to a plurality of sensor data sources arranged at locations within a plurality of regions of a site under surveillance. The surveillance system accesses a threat model that identifies contextual events classified as threats. The surveillance system identifies at least one contextual event for a site in real-time by processing sensor data generated by the sensor data sources, and co-occurring contextual data for at least one of the regions. Each identified contextual event is classified as one of a threat and a non-threat by using the threat model.