A computer-implemented method for segmenting and recombining video segments of an input video based on prerequisites is disclosed. The computer-implemented method includes classifying video segments of an input video with respective activities associated with the video segments. The computer-implemented method further includes determining one or more prerequisites for performing classified activities associated with video segments of the input video. The computer-implemented method further includes determining respective users which satisfy the one or more determined prerequisites for performing the classified activities associated with the video segments of the input video. The computer-implemented method further includes generating a new video for a user based, at least in part, on merging those video segments in which the user satisfies the one or more determined prerequisites for performing a classified activity associated with a video segment.

Systems and methods disclosed herein use a first machine learning architecture and a second machine learning architecture where the first machine learning architecture executes on a first processor and receives a first image representing a mouth of a user, determines user feedback for outputting to the user based on a first machine learning model, and outputs the user feedback for capturing a second image representing the mouth of the user. The second machine learning architecture executes on a second processor and receives the first image and the second image, and generates a 3D model of at least a portion of a dental arch of the user based on the first image and the second image where the 3D model is generated based on a second machine learning model of the second machine learning architecture.

A system and a method for automatic video redaction are provided herein. The method may include: receiving, an input video comprising a sequence of frames captured by a camera, wherein the input video includes live video obtained directly from the camera, wherein recordation of the video directly from the camera is disabled; performing visual analysis of the input video, to detect portions of the frames of the input video in which one of a plurality of predefined objects or a descriptor thereof is detected; generating a redacted input video by replacing the portions of the frames with new portions of another visual content; and recording the redacted input video on a data storage device, wherein the generating of thethe redacted input video, is carried out by a computer processor, after the input video is captured by the camera and before the recording of the redacted input video on the data storage device.

Systems, computer program products, and methods are described herein for preserving image and acoustic sensitivity using reinforcement learning. The present invention is configured to initiate a file editing engine on the audiovisual file to separate the audiovisual file into a video component and an audio component; initiate a convolutional neural network (CNN) algorithm on the video component to identify one or more sensitive portions in the one or more image frames; initiate an audio word2vec algorithm on the audio component to identify one or more sensitive portions in the audio component; initiate a masking algorithm on the one or more image frames and the audio component; generate a masked video component and a masked audio component based on at least implementing the masking action policy; and bind, using the file editing engine, the masked video component and the masked audio component to generate a masked audiovisual file.

Systems and methods for providing a universal platform for at-home health testing and diagnostics are provided herein. In particular, a health testing and diagnostic platform is provided to connect medical providers with patients and to generate a unique, private testing environment. In some embodiments, the testing environment may facilitate administration of a medical test to a patient with the guidance of a proctor. In some embodiments, the patient may be provided with step-by-step instructions for test administration by the proctor within a testing environment. The platform may display unique, dynamic testing interfaces to the patient and proctor to ensure proper testing protocols and accurate test result verification.

A human-object interaction detection method, a neural network and a training method therefor is provided. The human-object interaction detection method includes: extracting a plurality of first target features and one or more first motion features from an image feature of an image to be detected; fusing each first target feature and some of the first motion features to obtain enhanced first target features; fusing each first motion feature and some of the first target features to obtain enhanced first motion features; processing the enhanced first target features to obtain target information of a plurality of targets including human targets and object targets; processing the enhanced first motion features to obtain motion information of one or more motions, where each motion is associated with one human target and one object target; and matching the plurality of targets with the one or more motions to obtain a human-object interaction detection result.

Broadly speaking, the present techniques generally relate to a method and apparatus for video recognition, and in particular relate to a computer-implemented method for performing video recognition using a transformer-based machine learning, ML, model. Put another way, the present techniques provide new methods of image processing in order to automatically extract feature information from a video.

The present disclosure provides video processing methods, apparatuses and systems. The method includes: obtaining a to-be-processed video, where the to-be-processed video is obtained by performing feature removal processing for one or more objects in an original video; obtaining a feature restoration processing request for one or more to-be-processed objects; according to the feature restoration processing request for the one or more to-be-processed objects, obtaining feature image information corresponding to the one or more to-be-processed objects, where the feature image information for one of the one or more to-be-processed objects includes pixel position information of all or part of features for the one of the one or more to-be-processed objects in the original video; according to the feature image information for the one or more to-be-processed objects, performing feature restoration processing for the one or more to-be-processed objects in the to-be-processed video.

The present disclosure provides video processing methods, apparatuses and systems. The method includes: obtaining a to-be-processed video, where the to-be-processed video is obtained by performing feature removal processing for one or more objects in an original video; obtaining a feature restoration processing request for one or more to-be-processed objects; according to the feature restoration processing request for the one or more to-be-processed objects, obtaining feature image information corresponding to the one or more to-be-processed objects, where the feature image information for one of the one or more to-be-processed objects includes pixel position information of all or part of features for the one of the one or more to-be-processed objects in the original video; according to the feature image information for the one or more to-be-processed objects, performing feature restoration processing for the one or more to-be-processed objects in the to-be-processed video.

The present disclosure relates to methods and apparatuses for identifying an object of interest of a user. One example method includes obtaining information about a line-of-sight-gazed region of the user and an environment image corresponding to the user, obtaining information about a first gaze region of the user in the environment image based on the environment image, where the first gaze region is used to indicate a sensitive region determined by using a physical feature of a human body, and obtaining a target gaze region of the user based on the information about the line-of-sight-gazed region and the information about the first gaze region. The gaze region is used to indicate a region in which a target object gazed by the user in the environment image is located.