Abstract: Techniques relate to object classifications using bootstrapping of region-level annotations. For each of multiple images, regions within the image can be identified. For each region, a region-specific label can be identified, a set of objects within the region can be detected, and an object-specific label can be assigned to each object. The object-specific label can be the same as the region-specific label assigned to the region within which the object is located. A training data set can be defined to include, for each image of the multiple images, object-location data (indicating intra-image location data for the detected object) and label data (indicating the object-specific labels assigned to the objects). An image-processing model can be trained using the training data. Training can include learning values for a set of parameters that define calculations performed by the image-processing model.

Abstract: Human or object presence in or absence from a field-of-view of a camera can be achieved by analyzing camera data using a processor inside of or adjacent to the camera itself. In an example, a video signal processing system receives image data from one or more cameras and uses a processing circuit to determine whether a designated object is or is not present at a particular time, during a particular interval, or over a designated sequence of frames. In an example, the designated object can include one or more of a human being, a vehicle, or a parcel. In an example, results of the determination, such as including positive human or object identification, can be used as a trigger for operation of a barrier or access door.

Abstract: An example system includes a processor to automatically extract text and images from a document. The processor can automatically generate text bags including a number of nearest texts for each of the extracted images. The processor can then train a multi-modal model based on the automatically generated text bags using a CLIP-MIL loss that computes, for each of the extracted images, a correlation between each of the different texts in the texts bags using a CLIP feature space at each gradient step of the gradient descent-based multiple instance learning (MIL) algorithm.

Abstract: Methods, systems, and devices may address issues for sending a paging message to a remote UE that is more than one hop from a base station.

Abstract: The present invention provides a SoC including a recognition circuit and a processing circuit. The recognition circuit is configured to obtain image data from an image capturing device, and perform a recognition operation on the image data to generate a recognition result. The processing circuit is coupled to the recognition circuit, and is configured to determine a ROI in the image data according to the recognition result, perform image enhancement operation on the ROI to generate an enhanced region, and combine the enhanced region with the image data to generate processed image data.

Abstract: A network parameter configuration method includes obtaining operational state information of a network device. The operational state information includes a state information set of each of N areas covered by the network device, and the state information set includes state information subsets in D dimensions. Negotiations are performed based on the state information subsets in the D dimensions of each of the N areas to obtain target parameter subsets in the D dimensions of each of the N areas. A configuration instruction is sent to the network device covering the N areas to instruct the network device to perform parameter configuration based on a target parameter set of each of the N areas that is carried in the configuration instruction. The method can improve network parameter configuration efficiency.

Abstract: A method of data augmentation is provided. The method includes the following operations: selecting an original image from an original dataset including label data configured to indicate a labeled area of the original image; selecting at least part of content, located in the labeled area, of the original image as a first target image; generating a first sample image according to the first target image, in which the first sample image includes the first target image and a first border pattern different from the first target image, and the content, located in the labeled area, of the original image is free from including at least part of the first border pattern; and incorporating the first sample image into a sample dataset, in which the sample dataset is configured to be inputted to a machine learning model.

Abstract: Methods and systems are disclosed for automatic generation of content distribution images that include receiving user input corresponding to a content-distribution operation. The user input may be parsed to identify keywords. Image data corresponding to the keywords can be identified. Image-processing operations may be executed on the image data. Executing a generative adversarial network on the processed image data, which includes: executing a first neural network on the processed-image data to generate first images that correspond to the keywords, the first images generated based on a likelihood that each image of the first images would not be detected as having been generated by the first neural network. A user interface can display the first images with second images that include images that were previously part of content-distribution operations or images that were designated by an entity as being available for content-distribution operations.

Abstract: The disclosed technology provides for generating best images of a person in a retail environment. A method may include receiving, by an edge computing device from a camera, a continuous stream of image data of the retail environment, detecting, using object detection techniques, a person in the image data, the image data including a group of images that are part of a time series, generating bounding boxes for each of the group of images around the person based on detecting the person as they move in the images, identifying, based on applying a features model to each bounding box, at least one feature of the group of images depicting the person, selecting a subset of the bounding boxes having at least one feature that satisfies best images criteria, the subset having best images of the person, and returning the best images of the person.

Abstract: An iris detection method includes acquiring a first image; generating a second image including an eye section of the person from the first image; generating a third image where pixels having a gradation value smaller than a threshold value are represented by a first luminance value and pixels having a gradation value equal to or greater than the threshold value are represented by a second luminance value after binarizing the second image; generating a fourth image by replacing the pixel of the second luminance value come into existence in a first luminance area having the first luminance value and satisfying a predetermined condition with the pixel of the first luminance value in the third image; calculating iris information including information about at least one of a position and a size of the iris of the person by use of the fourth image; and outputting the iris information.

Abstract: A method of controlling a home appliance connected to the Internet via a gateway and an appliance control server of an appliance control cloud. The appliance executes control commands for operating the appliance received from the Internet via the gateway only if the commands are received from the appliance control cloud. A control command supply server of a control command supply cloud is connected to the home appliance control cloud via the Internet, and has at least one repository storing control instruction sets to operate the appliance. The method includes authenticating a request for remote control of the appliance received from the control command supply server via the Internet, and, responsive to positive authentication, forwarding control commands from the control command supply server to the appliance. The control commands are part of a set of instructions from the repository selected based on input received by a user terminal device.

Abstract: In some instances, a method is provided. The method comprises: obtaining, by a computing system, a plurality of documents, wherein at least one document, of the plurality of documents, comprises one or more circular shaped user indications, wherein each of the one or more circular shaped user indications indicates a user selection of a design or text within the associated circular shaped user indication; determining, by the computing system, circular shaped identification information for a document, of the plurality of documents based on inputting the document into a trained machine learning—artificial intelligence (ML-AI) model, wherein the circular shaped identification information indicates the user selection of the design or the text within the associated circular shaped user indication; and performing, by the computing system, one or more actions based on the circular shaped identification information.

Abstract: This application discloses a neural network construction method and apparatus, and an image processing method and apparatus in the field of artificial intelligence. The neural network construction method includes: constructing a search space based on an application requirement of a target neural network, where the search space includes M elements, the M elements are used to indicate M network structures, each of the M elements includes a quantity of blocks in a stage in a corresponding network structure and a channel quantity of each block, and M is a positive integer; and selecting a target network structure from the M network structures based on a distribution relationship among unevaluated elements in the search space. According to the method, a neural network satisfying a performance requirement can be efficiently constructed.

Abstract: A method for configuring an object detection system includes providing annotated training data comprising image data with defined assignments to at least one object, and training a neural network with a first neural sub-network, which is provided to compress the image data. The first neural sub-network is connected to at least one further neural sub-network. The at least one further neural sub-network is configured to detect an object from the compressed training data. The first neural sub-network is parameterized in such a manner that the object is detected using the at least one further sub-network in a defined quality. The neural sub-networks are trained jointly.

Abstract: A method for supporting activation/deactivation of serving cells by a base station (BS) in a wireless communication system provides decreased overhead and decreased power consumption for a user equipment (UE). The method includes configuring M supportable serving cells in the UE, configuring an indicator indicating activation/deactivation of each of the M serving cells, configuring a medium access control (MAC) message which includes a MAC control element (CE) and a logical channel identifier (LCID), the MAC CE including the indicator configured for each of the M serving cells and having a length corresponding to an integer multiple of 8 bits, the LCID indicating that the MAC CE includes the indicator indicating activation/deactivation of each serving cell, and transmitting the configured MAC message to the UE. Accordingly, a control channel or data channel regarding a component carrier is selectively received depending on whether the component carrier is activated.

Abstract: Methods, systems, and computer program products are provided for signature verification. Signature verification may be provided for target signatures using genuine signatures. A signature verification model pipeline may extract features from a target signature and a genuine signature, encode and submit both to a neural network to generate a similarity score, which may be repeated for each genuine signature. A target signature may be classified as genuine, for example, when one or more similarity scores exceed a genuine threshold. A signature verification model may be updated or calibrated at any time with new genuine signatures. A signature verification model may be implemented with multiple trainable neural networks (e.g., for feature extraction, transformation, encoding, and/or classification).

Abstract: Embodiments described herein provide systems and techniques for tracking and de-identifying person in a captured operating room (OR) video. In one aspect, a process for de-identifying OR personnel in an OR video begins by simultaneously receiving a color image captured by an RGB camera and a depth image captured by a depth camera installed in the vicinity of the RGB camera. The process then generates a 3D point cloud based on the depth image. Next, the process applies a human-body detector to the 3D point cloud to detect a set of 3D bodies in the 3D point cloud, wherein each detected 3D body corresponds to a detected person in the OR. The process next projects each detected 3D body into a 2D body outline in the color image to represent the same detected person in the color image. The process subsequently de-identifies the detected people in the color image based on the projected 2D body outlines.

Abstract: A computer-implemented method of detecting distracted driving comprises: determining, by one or more processors, a primary preview region (PPR) in a representation of an environment; determining, by the one or more processors, a gaze point for a driver based on a sequence of images of the driver; determining, by the one or more processors, that the gaze point is outside of the PPR; based on the determined gaze point being outside of the PPR, decreasing, by the one or more processors, an attention level for the PPR; based on the attention level for the PPR, generating, by the one or more processors, an alert.

Abstract: A method of detecting objects in a video includes, for each image in the video, determining a degree of correlation between the image and at least one preceding image in a sequence of images; and obtaining a first measure of energy consumption required for performing an object detection process on the image, obtaining a second measure of energy consumption required for performing an object tracking process for tracking at least one object from a previous image, and comparing the first and second measures of energy. The object detection process is performed on the image if either said degree of correlation is below a threshold level or said first measure of energy is lower than the second measure of energy, and otherwise the object tracking process is performed on the image.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that decode purchase data using an image. An example apparatus includes processor circuitry to execute machine readable instructions to at least crop an image of a receipt based on detected regions of interest, apply a first mask to a first cropped image to generate first bounding boxes corresponding to rows of the receipt, apply a second mask to a second cropped image to generate second bounding boxes corresponding to columns of the receipt, generate a structure of the receipt by mapping words detected by an optical character recognition engine to corresponding first bounding boxes and second bounding boxes based on a mapping criterion, classify the second bounding boxes by identifying an expression of interest in ones of the second bounding boxes, and generate purchase information by extracting text of interest from the structured receipt based on the classifications.