Abstract: A person re-identification method and apparatus, a device, and a readable storage medium. A homogeneous training network of an initial person re-identification network is trained by means of an objective function such as a knowledge synergy for dynamic classification probability loss function to obtain a final person re-identification network carrying more accurate final weight parameters, and a person re-identification task is performed by means of the final person re-identification network. In this way, the accuracy and performance of the person re-identification network to process the person re-identification task may be improved, the storage space in a device may be reduced, more beneficial to the storage and deployment of the portable device, and the amount of calculation of performing the person re-identification task may be reduced, thereby increasing the processing rate of the person re-identification task.

Abstract: The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.

Abstract: A method includes receiving a first time series of video frames depicting a first area of interest that includes a display area in a retail environment, and using classification by a convolutional neural network to detect instances of people picking up inventory items from the display area. The method also includes receiving a second time series of video frames depicting a second area of interest in the retail environment, and determining, based upon one or more portions of the second time series of video frames, that at least one inventory item picked up at the display area was not, or will not likely be, checked out. The method also includes causing one or more alert messages to be displayed, including causing an alert message to be displayed based on the determining.

Abstract: Systems and methods for detecting/identifying novel material samples are provided. A test sample image is processed with a trained transformation function to obtain a transformed matrix. A measure of similarity of the test image based on the transformed matrix is compared to a threshold to determine whether the test sample is novel to a batch of material samples that are provided to train the transformation function.

Abstract: A first processing unit detects each of a plurality of patterns included in the image as one of families by subjecting the image to a neural network process. An extraction unit extracts a plurality of parts that include families from the image, based on a position of the families detected. A second processing unit acquires intermediate data for an intermediate layer unique to each family, by subjecting the plurality of parts extracted to a neural network process. A clustering unit subjects the intermediate data acquired to clustering in accordance with the number of types of pattern. A calculation unit calculates a number of patterns included in each cluster that results from clustering.

Abstract: A motion classification model learning apparatus that learns a model for early recognizing a motion is provided. A training data acquisition part acquiring training data configured with pairs of video information about a motion that can be classified into any of a plurality of categories according to characteristics of the motion and category information that is a correct label corresponding to the video information; a motion history image generation part generation a motion history image of the video information; and a model learning part learning a model that outputs a label that is the category information, with the motion history image as an input are included.

Abstract: A method, according to one example, includes receiving a 360-degree image that was captured by a 360-degree camera, converting the 360-degree image into a rectangular image, and copying an edge portion from a first edge of the rectangular image and pasting the copied edge portion to a second edge of the rectangular image, thereby forming a modified rectangular image. The method further includes applying a neural network to the modified rectangular image to identify objects appearing in the modified rectangular image, wherein the modified rectangular image facilitates object identification near the second edge.

Abstract: Simultaneous learning of a plurality of different tasks and domains, with low costs and high precision, is enabled. A learning unit 160, on the basis of learning data, uses a target encoder that takes data of a target domain as input and outputs a target feature expression, a source encoder that takes data of a source domain as input and outputs a source feature expression, a common encoder that takes data of the target domain or the source domain as input and outputs a common feature expression, a target decoder that takes output of the target encoder and the common encoder as input and outputs a result of executing a task with regard to data of the target domain, and a source decoder that takes output of the source encoder and the common encoder as input and outputs a result of executing a task with regard to data of the source domain, to learn so that the output of the target decoder matches training data, and the output of the source decoder matches training data.

Abstract: The disclosure relates to a system for automatic recognition of plants, including a central server unit, a network, an automatic picture processing unit, a plant database unit, in which plant data are stored, a picture database unit, in which picture data related to plants are stored, and with a mobile device with a camera, the camera of the mobile device configured to provide picture data of a plant and the picture data transmitted via the network to the server unit, the automatic picture processing unit together with the plant database unit and the picture database unit on the basis of the transmitted picture data is configured to perform a comparison with data in the plant database unit and the picture database unit and the transmitted picture data of the plant such, that the plant is defined and the definition is transmitted back to the mobile device.

Abstract: An embodiment provides a method of operating wire harness manufacturing equipment, including: adding, using the manufacturing equipment, an element to a wire to form a combination of the element and the wire; capturing, using an imaging device, one or more of an upper image and a lower image of the combination; analyzing, using one or more processors operatively coupled to the imaging device, the one or more of the upper image and the lower image to detect a defect; and thereafter indicating that the defect has been detected. Other embodiments are described and claimed.

Abstract: The disclosed subject matter relates to a method for determining a type and a state of an object of interest, comprising: generating a depth map of a scene by means of a depth sensor, the scene containing the object of interest and an occlusion object lying between the depth sensor and the object of interest; computing three 2D occupancy views, each in a different viewing direction, and three 2D occlusion views; feeding each of said occupancy and occlusion views into a different input of a trained convolutional neural network; receiving both a class and a bounding box of the object of interest from the convolutional neural network; and determining the type of the object of interest and the state of the object of interest. The disclosed subject matter further relates to a system for carrying out said method.

Abstract: A method and system may use machine learning analysis of audio data to automatically identify a user's biometric characteristics. A user's client computing device may capture audio of the user. Feature data may be extracted from the audio and applied to statistical models for determining several biometric characteristics. The determined biometric characteristic values may be used to identify individual health scores and the individual health scores may be combined to generate an overall health score and longevity metric. An indication of the user's biometric characteristics which may include the overall health score and longevity metric may be displayed on the user's client computing device.

Abstract: A method for generating adversarial attacks on a video recognition model is disclosed, including (a) generating the content of bullet-screen comments (BSCs) by an image captioning model for a clean video sample; (b) generating an adversarial video sample by inserting the BSCs into the clean video sample; (c) using the adversarial video sample to attack the video recognition model; (d) receiving rewards from the environment; (e) optimizing the position and transparency of BSCs by an reinforced learning (RL) agent according to the received rewards; (f) updating the adversarial video sample and using it to attack the video recognition model; and iteratively repeating steps of (d)-(f), until a predefined condition is matched.

Abstract: A method of facilitating image analysis in pathology involves receiving a sample image representing a sample for analysis, the sample image including sample image elements, causing one or more functions to be applied to the sample image to determine a plurality of property specific confidence related scores, each associated with a sample image element and a respective sample property and representing a level of confidence that the associated element represents the associated sample property, sorting a set of elements based at least in part on the confidence related scores, producing signals for causing one or more of the set of elements to be displayed to a user in an order based on the sorting, for each of the one or more elements displayed, receiving user input, and causing the user input to be used to update the one or more functions. Other methods, systems, and computer-readable media are disclosed.

Abstract: The purpose of the present invention is to enable learning of a neural network for extracting features of images having high robustness from an undiscriminating image region while minimizing the number of parameters of a pooling layer. A parameter learning unit 130 learns parameters of each layer in a convolutional neural network configured by including a fully convolutional layer for performing convolution of an input image to output a feature tensor of the input image, a weighting matrix estimation layer for estimating a weighting matrix indicating a weighting of each element of the feature tensor, and a pooling layer for extracting a feature vector of the input image based on the feature tensor and the weighting matrix.

Abstract: Sensor data, including a group of time series elements, is received. A training data set is determined, including by determining for at least a selected time series element in the group of time series elements a corresponding ground truth. The corresponding ground truth is based on a plurality of time series elements in the group of time series elements. A processor is used to train a machine learning model using the training dataset.

Abstract: A method, apparatus and a product for determining a performance measurement of predictors. The method comprises obtaining a dataset comprising data instances. Each data instance is associated with a label; obtaining a predictor. The predictor is configured to provide a prediction of a label for a data instance; determining a plurality of data slices that are subsets of the dataset. computing, for each data slice in the plurality of data slices and based on an application of the predictor on each data instance that is mapped to the data slice, a performance measurement that is indicative of a successful label prediction for a data instance comprised by the data slice, whereby obtaining a plurality of performance measurements; based on the plurality of performance measurements, computing a performance measurement of the predictor over the dataset; if the performance measurement of the predictor is below a threshold, performing a mitigating action.

Abstract: A method, apparatus and system for locating external apparatus mounted to a tubular in a wellbore. The identification of apparatus, such as cable clamps, enables other tools in the string to operate more precisely. A computer model is used to locate the apparatus from acoustic images, which images are acquired using a downhole device having an acoustic sensor or acoustic array. The model may be a classifier, which may be machine trained to classify whether an apparatus is present, its location and its orientation. Automating this locating enables very long wellbores to be processed quickly.

Abstract: The present disclosure relates to systems and methods for positioning a subject. The method may include generating a first image of the subject disposed on a scanning board of an imaging device. The first image may include position information of the subject. The method may further include generating a second image of the subject which includes information associated with one or more organs of the subjects. Additionally, the method may include determining the position of a ROI based on the first image and the second image. The method may further include operating the imaging device to scan a target portion of the subject.

Abstract: An example apparatus includes a feature extractor to generate a first image descriptor based on a first image of a first retail product tag corresponding to a first category, the first image descriptor representative of one or more visual features of the first retail product tag; a feature descriptor generator to generate a feature descriptor corresponding to the first retail product tag by concatenating the first image descriptor and a first category signature corresponding to the first retailer category; and a classifier to generate a first probability value corresponding to a first type of promotional product tag and a second probability value corresponding to a second type of promotional product tag based on the feature descriptor; and determine whether the first retail product tag corresponds to the first type of promotional product tag or the second type of promotional product tag based on the first and second probability values.