Abstract: Various disclosed embodiments can resolve output inaccuracies produced by many machine learning models. Embodiments use content order as input to machine learning model systems so that they can process documents according to the position or rank of instances in a document or image. In this way, the model is less likely to misclassify or incorrectly detect instances or the ordering between predicted instances. The content order in various embodiments can be used as an additional signal to classify or make predictions.

Abstract: Systems, device and techniques are disclosed for multi-label product categorization. A catalog entry and a list of categories may be received. The catalog entry may be associated with an item. A textual description may be generated by comparing words in the catalog entry to existing vocabularies of words and applying part-of-speech tagging to the catalog entry. A feature vector may be generated from the textual description by applying any of token frequency feature creation, term frequency-inverse document frequency feature creation, and pre-trained word embeddings to the textual description. A set of probabilities may be determined by inputting the feature vector into a machine learning model. The set of probabilities may include a probability for each category in the list of categories.

Abstract: A method for local approximation of a predictive model may include receiving unclassified data associated with a plurality of unclassified data items. The unclassified data may be classified based on a first predictive model to generate classified data. A first data item may be selected from the classified data. A plurality of generated data items associated with the first data item may be generated using a generative model. The plurality of generated data items may be classified based on the first predictive model to generate classified generated data. A second predictive model may be trained with the classified generated data. A system and computer program product are also disclosed.

Abstract: The present disclosure relates to methods and systems for identifying an individual in a crowd. The method comprises the steps of capturing a plurality of crowd-viewing images using multiple image-capturing devices. For each image, a reference point within a facial contour of a person in the image is selected. The reference point is selected based on a facial feature on the facial contour of the person. The method further comprises the step of processing the image to obtain one or more angular measurements associated with a posture displayed by the person in the image. Based on the obtained angular measurements, location data of a predetermined body point of the person relative to the reference point is extracted.

Abstract: A method for processing an image is presented. The method locates a subject and an object of a subject-object interaction in the image. The method determines relative weights of the subject, the object, and a context region for classification. The method further classifies the subject-object interaction based on a classification of a weighted representation of the subject, a weighted representation of the object, and a weighted representation of the context region.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for off-policy evaluation of a control policy. One of the methods includes obtaining policy data specifying a control policy for controlling a source agent interacting with a source environment to perform a particular task; obtaining a validation data set generated from interactions of a target agent in a target environment; determining a performance estimate that represents an estimate of a performance of the control policy in controlling the target agent to perform the particular task in the target environment; and determining, based on the performance estimate, whether to deploy the control policy for controlling the target agent to perform the particular task in the target environment.

Abstract: Provided are a face feature point detection method, applied to an image processing device, where the image processing device stores a feature area detection model and a feature point detection model. The method includes: preprocessing a face image to be detected to obtain a preprocessed target face image; performing feature point extraction on the target face image according to the feature area detection model and the feature point detection model to obtain a target feature point coordinate located within a face feature area in the target face image; and performing coordinate transformation on the target feature point coordinate to obtain a face feature point coordinate corresponding to the face image to be detected. Further provided are a face feature point detection device, an equipment and a storage medium.

Abstract: This inventions provides an artificial intelligence (A.I.) identified measuring method for a semiconductor image, comprising the steps of: providing an original image of a semiconductor; identifying a type and/or a category of the original image by an artificial intelligence; introducing a predetermined dimension measuring mode corresponding to the identified type and/or the identified category to scan the original image to generate a measurement signal of the original image; and extracting a designated object from the original image to generate a specific physical parameter of the original image after operation based on a measurement signal of the designated object and the measurement signal of the original image.

Abstract: A method for image processing includes: receiving a third two-dimensional image and a depth image corresponding to the third two-dimensional image, wherein the third two-dimensional image and the depth image include a face; establishing a three-dimensional model of the face according to the depth image; rotating the three-dimensional model of the face by a first angle; projecting the three-dimensional model of the face rotated by the first angle to an image coordinate system of the third two-dimensional image; and building a three-dimensional model of a background region of the third two-dimensional image, processing a background region of an image projected to the image coordinate system of the third two-dimensional image to obtain a fourth image.

Abstract: An image generating apparatus includes: a display outputting an image; a memory storing one or more instructions; and a processor. The processor is configured to execute the one or more instructions to detect an object in an image including a plurality of frames, provide a plurality of candidate boundaries for masking the detected object, identify an optimal boundary by assessing the provided plurality of candidate boundaries, and generate a partial moving image with the object moving by using the optimal boundary.

Abstract: Methods and systems for recognition of repeating geometric units in a computer image are here disclosed. According to an aspect, a representation of a computer image is split into components and every component is analyzed to identify the role of this component on the computer image. Identified components could include but are not limited to: rooms, doors, furniture etc. In one embodiment components are identified based on the presence or absence of at least one other component. The presence of a door, in a non-limiting example, could be used to denote the presence of a room. In a further embodiment, the program may extract size and perimeter information necessary for estimating needed materials from a representation of a computer image.

Abstract: A location information reporting method includes: a first base station receives a first message from a core network device, where the first message includes first instruction information used to instruct to obtain location information of a terminal device, and the first base station and the core network device have a control plane connection established for the terminal device; and the first base station sends a second message to a second base station, where the second message includes second instruction information, the second instruction information is used to instruct to obtain the location information of the terminal device, and the second base station is configured to provide a service for the terminal device at a current moment.

Abstract: According to one embodiment, a reading system includes an extractor, a determiner, and a reader. The extractor extracts a candidate image from an input image. The candidate image is a candidate of a portion of the input image in which a segment display is imaged. The determiner uses the candidate image and a mask to calculate a match ratio indicating a certainty of a segment display being included in the candidate image, and determines that the candidate image is an image of a segment display when the match ratio is not less than a threshold. The mask and the threshold are preset. The reader reads a numerical value displayed in a segment display from the candidate image determined to be an image of a segment display.

Abstract: An image processing apparatus includes a measurement area setting unit that sets a measurement area in an input image, a small image setting unit that sets a small image in the input image based on the measurement area, a first estimation unit that estimates a flow distribution of a target in the small image, and a second estimation unit that estimates the number of targets to pass through the measurement area based on the flow distribution in the small image.

Abstract: A computing system is disclosed including a convolutional neural configured to receive an input that describes a facial image and generate a facial object recognition output that describes one or more facial feature locations with respect to the facial image. The convolutional neural network can include a plurality of convolutional blocks. At least one of the convolutional blocks can include one or more separable convolutional layers configured to apply a depthwise convolution and a pointwise convolution during processing of an input to generate an output. The depthwise convolution can be applied with a kernel size that is greater than 3×3. At least one of the convolutional blocks can include a residual shortcut connection from its input to its output.

Abstract: In a communications method of a wearable device, if a second BLUETOOTH connection between user equipment and the wearable device is disconnected, a first BLUETOOTH connection between a network connection device and the wearable device is established, so that the network connection device may send, to the wearable device through the first BLUETOOTH connection, first information sent by the user equipment through a third communication connection to the network connection device. In this way, indirect communication between the user equipment and the wearable device is implemented by using the network connection device, so that the user equipment and the wearable device are not limited by a communication range.

Abstract: A method and system generate an ensemble image representation for cross-domain retrieval of a fashion item image from a database by using a three-stream Siamese triplet loss trained convolutional neural network to generate a first retrieval descriptor corresponding to an inputted query image; using an average precision loss trained convolutional neural network to generate a second retrieval descriptor corresponding to the inputted query image; concatenating both the first retrieval descriptor and the second retrieval descriptor; and I2-normalizing the concatenated result to generate the ensemble image representation. During a first stage of the method and system, database items are cropped using a trained fine-grained fashion item detector.

Abstract: A method and an apparatus for outputting data are provided. The method includes: obtaining a set of human-face key point data, where the human-face key point data characterizes a position of a key point of a human face in a target human-face image; determining human-eye feature data for characterizing a shape feature of a human eye, based on the set of the human-face key point data; and inputting the human-eye feature data into a human-eye size recognition model obtained by pre-training to obtain a degree value for characterizing a size of the human eye, and outputting the degree value. The human-eye size recognition model characterizes a correspondence between human-eye feature data and a degree value. With the above method, the human-face key point data is effectively utilized to determine the size of the human eye, improving the accuracy of recognizing the size of the human eye.

Abstract: Disclosed are systems and methods for detecting a printer jam. The systems and methods can include receiving a reference image of a printer opening and a second image of the printer opening. A lack of a receipt between the second image compared to the reference image can be detected. The lack of the receipt can indicate the printer jam. After detecting the printer jam, operation of the self-service terminal can be terminated.

Abstract: A method includes training, using at least one processor, a specialized teacher model to perform visual object instance segmentation in order to segment and classify objects in first training images. The first training images contain foreground objects without backgrounds. The method also includes training, using the at least one processor, a student model to perform visual object instance segmentation in order to segment and classify objects in second training images. The second training images contain the foreground objects and the backgrounds. Training the student model includes using selected outputs of the specialized teacher model. The method further includes deploying the trained student model to perform visual object instance segmentation in an external device.