Abstract: Systems and methods for adapting physical activities and exercises based on facial analysis by image processing are disclosed. A method includes: identifying, by a computer device, a user; receiving, by the computer device, video data of a face region of the user while the user is engaged in exercise or physical activity; analyzing, by the computer device, the video data to determine a detected state of the user, wherein the analyzing the video data includes performing facial analysis using the video data; and providing, by the computer device, feedback to the user based on the analyzing the video data.

Abstract: A calibration device for calibrating detection processing of a line of sight of a user can perform first calibration processing in which an instruction to look at each of a plurality of positions by moving the face is given and second calibration processing in which an instruction to look at each of the plurality of positions without moving the face is given. The calibration device executes both the first calibration processing and the second calibration processing when it is determined that an eye of the user can be detected, and executes the first calibration processing and cease to execute the second calibration processing when it is determined that an eye of the user cannot be detected.

Abstract: Systems, methods, devices, media, and computer readable instructions are described for local image tagging in a resource constrained environment. One embodiment involves processing image data using a deep convolutional neural network (DCNN) comprising at least a first subgraph and a second subgraph, the first subgraph comprising at least a first layer and a second layer, processing, the image data using at least the first layer of the first subgraph to generate first intermediate output data; processing, by the mobile device, the first intermediate output data using at least the second layer of the first subgraph to generate first subgraph output data, and in response to a determination that each layer reliant on the first intermediate data have completed processing, deleting the first intermediate data from the mobile device. Additional embodiments involve convolving entire pixel resolutions of the image data against kernels in different layers if the DCNN.

Abstract: The present disclosure provides a pedestrian re-identification method and apparatus. The pedestrian re-identification apparatus detects a pedestrian to be identified in a specified video frame, and within a specified time period before the capture time of the specified video frame, extracts all pedestrians appearing in the captured video frame and taking them to be historical pedestrians; calculating the feature distances between the pedestrian to be identified and the historical pedestrians; in order of feature distance, from small to large, extracting historical pedestrian identifications corresponding to a predetermined number of feature distances, and clustering the extracted historical pedestrian identifications so as to re-identify the pedestrians to be identified. The present disclosure effectively suppresses external noise interference and improves the success rate of pedestrian re-identification.

Abstract: This document relates to training of machine learning models. One example method involves providing a machine learning model having one or more mapping layers. The one or more mapping layers can include at least a first mapping layer configured to map components of pretraining examples into first representations in a space. The example method also includes performing a pretraining stage on the one or more mapping layers using the pretraining examples. The pretraining stage can include adding noise to the first representations of the components of the pretraining examples to obtain noise-adjusted first representations. The pretraining stage can also include performing a self-supervised learning process to pretrain the one or more mapping layers using at least the first representations of the training data items and the noise-adjusted first representations of the training data items.

Abstract: Systems and methods for estimating a layout of a room are disclosed. The room layout can comprise the location of a floor, one or more walls, and a ceiling. In one aspect, a neural network can analyze an image of a portion of a room to determine the room layout. The neural network can comprise a convolutional neural network having an encoder sub-network, a decoder sub-network, and a side sub-network. The neural network can determine a three-dimensional room layout using two-dimensional ordered keypoints associated with a room type. The room layout can be used in applications such as augmented or mixed reality, robotics, autonomous indoor navigation, etc.

Abstract: In one aspect, a method includes obtaining videos and for each video: obtaining a set of anchors for the video, each anchor beginning at the playback time and including anchor text; identifying, from text generated from audio of the video, a set of entities specified in the text, wherein each entity in the set of entities is associated with a times stamp at which the entity is mentioned; determining, by a language model and from the text generated from the audio of the video, an importance value for each entity; for a subset of the videos, receiving rater data that describes, for each anchor, the accuracy of the anchor text in describing subject matter of the video; and training, using the human rater data, the importance values, the text, and the set of entities, an anchor model that predicts an entity label for an anchor for a video.

Abstract: A computer system can implement a network service by receiving, from a computing device of a user, image data comprising an image of a record. The computer system can then execute image processing logic to determine a set of information items from the image. The computer system may then execute augmentation logic to process the record by (i) accessing a transaction database to identify a plurality of transactions made by the user, (ii) identifying a matching transaction from the plurality of transactions that pertains to the record, and (iii) resolving the set of information items using the matching transaction.

Abstract: There is provided a method and a system configured obtain an image of a semiconductor specimen including one or more arrays, each including repetitive structural elements, and one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements, wherein the PMC is configured to, during run-time scanning of the semiconductor specimen, perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix, use the correlation matrix to distinguish between one or more first areas of the image corresponding to the one or more arrays and one or more second areas of the image corresponding the one or more regions, and output data informative of the one or more first areas of the image.

Abstract: A data processing apparatus comprises a processing unit to generate image data for rendering data for display as an image frame, a memory to store the image data, the image data comprising data for a given object in the image frame, detection circuitry to detect, for the data for the given object, one or more properties associated with the given object in the image frame, prediction circuitry to generate, based on one or more of the properties, a confidence score for the data for the given object indicative of a likelihood that the data for the given object is to be used for more than a threshold number of image frames in a sequence of image frames, and allocation circuitry to allocate indicator data to the data for the given object, the indicator data indicative of a magnitude of the confidence score for the data for the given object, and to store the data for the given object in the memory in association with the corresponding indicator data.

Abstract: Provided is a method for developing a geographic agnostic machine learning model. The method may include selecting transaction data associated with payment transactions conducted by a first plurality of users, wherein the transaction data includes first transaction data associated with payment transactions conducted by a first plurality of users in a first geographic area and second transaction data associated with payment transactions conducted by a second plurality of users in a second geographic area, formatting the first transaction data associated with payment transactions conducted by the first plurality of users in the first geographic area and the second transaction data associated with payment transactions conducted by the second plurality of users in the second geographic area to provide training data, and generating the geographic agnostic machine learning model using the training data. A system and computer program product are also disclosed.

Abstract: Disclosed is a method for scheduling of shooting of a satellite image based on deep learning, which is performed by a computing device. The method may include: generating a prediction image and a cloud amount prediction value up to a future time desired by a user based on a pre-shot satellite image by using a pre-trained neural network model; and determining a shooting schedule of a satellite for at least one region of interest based on the cloud amount prediction value.

Abstract: A terminal apparatus includes a controller configured to: transmit, to a server apparatus, a suggestion request requesting a suggestion of a function to be invoked; receive, from the server apparatus, a suggestion response that specifies a suggested function determined by the server apparatus; and invoke the suggested function. The suggestion request includes face image data of a user. The suggested function is determined by applying the face image data to a learned model. The controller is configured to: evaluate, based on face images of the user, a degree of change in facial expressions before and after invoking the suggested function; and transmit, to the server apparatus, a learning request requesting re-learning of the learned model when the evaluated degree of change in the facial expressions satisfies a predetermined condition.

Abstract: A clustering method includes: multiple data to be clustered are acquired, the data to be clustered including exact matching information; multiple data sets consisting of data to be clustered with same exact matching information are determined; a clustering center is determined according to the amount of data to be clustered in each of the multiple data sets; and the multiple data to be clustered are clustered according to the clustering center to obtain target clusters. Accordingly, multiple data sets each consisting of data to be clustered with same exact matching information are determined, and a clustering center is determined according to the amount of the data to be clustered in each of the multiple data sets, so that unreasonable determination of the clustering centers can be improved, and accuracy of a clustering algorithm can further be improved.

Abstract: A method of monitoring a user performing a handwashing procedure, including: recognizing a user based upon biometric information obtained at a handwashing station, a user; detecting use of a component of the handwashing station; capturing image data of the user at the handwashing station; capturing data describing components and consumables used at the handwashing station; analyzing, using a processor, the image data; determining compliance with a handwashing procedure based at least in part on the analysis of the image data and the use of the component; and communicating compliance information.

Abstract: Samples at a semiconductor wafer that have been reviewed by a review tool may be identified. Furthermore, a candidate sample at the semiconductor wafer that has not been reviewed by the review tool may be identified. A location of the candidate sample at the semiconductor wafer may and a number of the samples that have been reviewed that are at locations proximate to the location of the candidate sample may be determined. The candidate sample may be selected for review by the review tool based on the number of the plurality of samples that are at locations proximate to the location of the candidate sample.

Abstract: Embodiments of the present disclosure provide systems and methods for recognizing a masked face. According to the present disclosure, the disclosed systems and methods include features that provide augmentation of existing face recognition databases, real-time mask detection, and real-time masked face recognition. In embodiments, masked face recognition includes a multi-layered approach, which includes finding matching simulated masked faces in the database that match the masked face being analyzed, comparing the unmasked portion of the masked face to stored unmasked faces in a database to identify any matches, and executing face restoration algorithms in which the masked portion is reconstructed to generate an unmasked representation which may then be matched against unmasked faces in the database.

Abstract: Systems, methods, devices, media, and computer readable instructions are described for local image tagging in a resource constrained environment. One embodiment involves processing image data using a deep convolutional neural network (DCNN) comprising at least a first subgraph and a second subgraph, the first subgraph comprising at least a first layer and a second layer, processing, the image data using at least the first layer of the first subgraph to generate first intermediate output data; processing, by the mobile device, the first intermediate output data using at least the second layer of the first subgraph to generate first subgraph output data, and in response to a determination that each layer reliant on the first intermediate data have completed processing, deleting the first intermediate data from the mobile device. Additional embodiments involve convolving entire pixel resolutions of the image data against kernels in different layers if the DCNN.

Abstract: Disclosed herein are system, method, and computer program product embodiments for securing ATM transactions based on facial detection. The approach contemplates performing facial detection on an image of a user of an ATM when the user authenticates themselves, and comparing the user's detected face to facial detection results of subsequent images taken at the ATM. Based on this comparison, various security events may be detected and, for any given security event, a particular action may be taken.

Abstract: The present disclosure provides a processor, a display driving circuit, and an electronic device. The processor includes: a memory being divided into a first part that stores correction data for correcting an image signal, a second part that stores data of an image signal to be displayed, and a third part that stores data of an application to be executed; and an output port configured to transmit the data of the image signal and the correction data stored in the memory to an external display driving circuit, separately.