A method may include receiving an event message from a home security edge device, including image data. The method may include determining whether the image data represents a false trigger event based on inputting the image data into an artificial intelligence model and/or receiving a user input. The user input may be responsive to a presentation of the image data. If the image data represents the false trigger event, the method may include generating training data for retraining the artificial model. The training data may include a portion of the image data. The method may include updating a local dataset to include the training data and training the artificial intelligence model. The method may include transmitting the training data to a central database. If the image data does not represent a false trigger event, the method may include providing a security alert for display on one or more user devices.

A vision inspection system includes a vision inspection controller receiving images form an imaging device. The vision inspection controller includes a binary classification tool and a multi-classification tool. The vision inspection controller processes each of the images through the binary classification tool to detect for the defects to determine primary inspection results including a PASS result if no defects are detected and a FAIL result if defects are detected. The vision inspection controller processes each of the images associated with the FAIL result through the multi-classification tool to determine secondary inspection results including identification of a type of defect. The vision inspection system may include a display configured to display the primary and secondary inspection results to an operator.

A vision inspection system includes a vision inspection controller receiving images form an imaging device. The vision inspection controller includes a binary classification tool and a multi-classification tool. The vision inspection controller processes each of the images through the binary classification tool to detect for the defects to determine primary inspection results including a PASS result if no defects are detected and a FAIL result if defects are detected. The vision inspection controller processes each of the images associated with the FAIL result through the multi-classification tool to determine secondary inspection results including identification of a type of defect. The vision inspection system may include a display configured to display the primary and secondary inspection results to an operator.

A computing entity is described that obtains at least one inspection image of an at least partially fabricated product and causes the at least one inspection image to be processed by a product inspection engine. The product inspection engine includes a machine learning-trained model. The computing entity obtains an inspection result determined based on the processing of the at least one inspection image by the product inspection engine; identifies one or more training images stored in an image database based at least in part on the at least one inspection image; associates automatically generated labeling data with the one or more training images based at least in part on the inspection result determined by the processing of the at least one inspection image; and causes training of the product inspection engine using the one or more training images and the associated labeling data.

Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for generating an avatar. The method includes generating an indication of correlation among image information, audio information, and text information of a video. The method may further include generating, based on the indication of the correlation, a first feature set and a second feature set representing features of a target object in the video, wherein the first feature set represents invariant features of the target object in the video, and the second feature set represents equivariant features of the target object in the video. The method may further include generating the avatar based on the first feature set and the second feature set. With this method, the generated avatar can be made more accurate and vivid with a better effect, while also reducing data annotation cost, improving operation efficiency, and enhancing user experience.

An apparatus for generating a user's video avatar, wherein the apparatus comprises a processor communicatively connected to a memory, wherein the memory instructs the processor to receive, from a user application and a user input device, user data, validate a user's identity as a function of the user data, and generate a video avatar as a function of the identity validation and user data.

An apparatus for generating a user's video avatar, wherein the apparatus comprises a processor communicatively connected to a memory, wherein the memory instructs the processor to receive, from a user application and a user input device, user data, validate a user's identity as a function of the user data, and generate a video avatar as a function of the identity validation and user data.

In variants, the method for subjective property scoring can include determining an objective score for a subjective characteristic of a property using a model trained using subjective property rankings.

In variants, the method for subjective property scoring can include determining an objective score for a subjective characteristic of a property using a model trained using subjective property rankings.

The present invention provides a robust and effective solution to an entity or an organization by enabling them to implement a system for swapping one or more faces without any explicit training on the one or more faces. The proposed method can be further implemented in real time.