Abstract: There are numerous features in video that can be detected using computer-based systems, such as objects and/or motion. The detection of these features, and in particular the detection of motion, has many useful applications, such as action recognition, activity detection, object tracking, etc. The present disclosure provides a neural network that learns motion from unlabeled video frames. In particular, the neural network uses the unlabeled video frames to perform self-supervised hierarchical motion learning. The present disclosure also describes how the learned motion can be used in video action recognition.

Abstract: A system for locating according to a data description includes an interface and a processor. The interface is configured to receive the data description. The processor is configured to create a model-based item identification job based at least in part on the data description; provide the model-based item identification job to a set of vehicle event recorder systems, wherein the model-based item identification job uses a model to identify sensor data resembling the data description; receive the sensor data from the set of vehicle event recorder systems; and store the sensor data associated with the model-based item identification job.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for selecting hyper-parameter sets by utilizing a modified Bayesian optimization approach based on a combination of accuracy and training efficiency metrics of a machine learning model. For example, the disclosed systems can fit accuracy regression and efficiency regression models to observed metrics associated with hyper-parameter sets of a machine learning model. The disclosed systems can also implement a trade-off acquisition function that implements an accuracy-training efficiency balance metric to explore the hyper-parameter feature space and select hyper-parameters for training the machine learning model considering a balance between accuracy and training efficiency.

Abstract: The present invention relates generally to object annotation, specifically to polygonal annotations of objects. Described are methods of annotating an object including steps of receiving an image depicting an object, generating a set of image features using a CNN encoder implemented on one or more computers, and producing a polygon object annotation via a recurrent decoder or a Graph Neural Network. The recurrent decoder may include a recurrent neural network, a graph neural network or a gated graph neural network. A system for annotating an object and a method of training an object annotation system are also described.

Abstract: An apparatus including an interface and a processor. The interface may be configured to receive pixel data generated by a capture device. The processor may be configured to generate video frames in response to the pixel data, perform computer vision operations on the video frames to detect objects, perform a classification of the objects detected based on characteristics of the objects, determine whether the classification of the objects corresponds to a user-defined event and a user-defined identity and generate encoded video frames from the video frames. The encoded video frames may comprise a first sample of the video frames selected at a first rate when the user-defined event is not detected and a second sample of the video frames selected at a second rate while the user-defined event is detected. The video frames comprising the user-defined identity without a second person may be excluded from the encoded video frames.

Abstract: Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification are disclosed. An example apparatus to classify an image includes an image crop detector to detect a first image crop from the image, the first image crop corresponding to a first object, a grouping controller to select a second image crop corresponding to a second object at a location of the first object, a prediction generator to, in response to executing a trained model, determine a label corresponding to the first object and a confidence level associated with the label, and a confidence recalibrator to recalibrate the confidence level based on a probability of the first object having a first attribute based on the second object having a second attribute, the confidence level recalibrated to increase an accuracy of the image classification.

Abstract: Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

Abstract: The present disclosure provides a system, a method and an apparatus for object identification, capable of solving the problem in the related art that a system for centralized control and management of unmanned vehicles may not be able to identify an object effectively. The system for object identification includes a sensing device, a control device and one or more unmanned vehicles.

Abstract: A system, article, and method of neural network object recognition for image processing includes customizing a training database and adapting an instance segmentation neural network used to perform the customization.

Abstract: A method for generating a tag of a video, an electronic device, and a storage medium are related to a field of natural language processing and deep learning technologies. The detailed implementing solution includes: obtaining multiple candidate tags and video information of the video; determining first correlation information between the video information and each of the multiple candidate tags; sorting the multiple candidate tags based on the first correlation information to obtain a sort result; and generating the tag of the video based on the sort result.

Abstract: An optical fingerprint authentication device includes at least a light source and an image sensor and detects diffused light. The light source is an organic electroluminescence panel. The organic electroluminescence panel comprises a light emitting portion region and a light-transmitting non-light emitting portion, the light emitting portion region being shaped by an organic electroluminescence element. A fingerprint information reader having the image sensor arranged at a position adjacent to the non-light emitting portion is provided.

Abstract: A method for distinguishing a real three-dimensional object from a two-dimensional spoof of the real object, the method comprising: obtaining, by an optical sensor of a mobile device, an image containing either the spoof or the real object; providing the image to a neural network; processing the image by the neural network by: calculating at least one of: 1) a distance map representative of the distance of a plurality of pixels to the optical sensor; or 2) a reflection pattern representative of light reflection associated with a plurality of pixels constituting at least a portion of the object within the image; and comparing at least one of the calculated distance map or the calculated reflection pattern with a learned distance map or a learned reflection pattern, to determine that the image contains either the spoof or the real object.

Abstract: A fingerprint sensing system. The fingerprint sensing system includes: at least one sensor; at least one display device; at least one application processor; and at least one secure enclave processor. The application processor(s) receives fingerprint data from the sensor(s) and provides the fingerprint data to the secure enclave processor(s). The secure enclave processor(s) decodes the fingerprint data and provides a signal indicative of at least one matched node. The application processor(s), responsive to receipt of the signal indicative of the matched node(s), presents at least a portion of a synthetic fingerprint image via at least one display device corresponding to the matched node(s).

Abstract: A display device including a substrate including a display region and a non-display region disposed at one side of the display region, the display region includes first and second areas adjacent to each other; a display unit disposed on a front surface of the substrate, the display unit including a plurality of pixels for displaying an image; a cushion layer disposed on a rear surface of the substrate, the cushion layer including a first opening portion corresponding to the first area; a fingerprint sensor provided in the first opening portion of the cushion layer, the fingerprint sensor sensing a fingerprint of a user; and a first light-blocking layer disposed on a rear surface of the substrate, the first light-blocking layer covering at least part of the cushion layer and a lower surface of the fingerprint sensor.

Abstract: The embodiment of the present application relates to the field of substance ingredient detection, for example, relates to a substance ingredient detection method and apparatus, and a detection device. The method includes: obtaining spectral information of a substance to be detected; and matching the spectral information with a pre-obtained prediction model based on a machine learning algorithm to obtain the ingredients of the substance to be detected. In the embodiment of the present application, the spectral information of the substance to be detected is obtained, and then the spectral information is matched with the prediction model based on the machine learning algorithm to obtain the prediction result of the ingredients of the substance to be detected. In the embodiment of the present application, the machine learning algorithm is combined with spectral recognition, the traditional algorithm is abandoned, the recognition speed is improved, and the substance detection efficiency is greatly improved.

Abstract: A method, a computer program product, and a computer system determine when to perform a federated learning process. The method includes identifying currently available contributors among contributors of a federated learning task for which the federated learning process is to be performed. The method includes determining a usefulness metric of the currently available contributors for respective datasets from each of the currently available contributors used in performing the federated learning process. The method includes, as a result of the usefulness metric of the currently available contributors being at least a usefulness threshold, generating a recommendation to perform the federated learning process with the datasets of the currently available contributors. The method includes transmitting the recommendation to a processing component configured to perform the federated learning process.

Abstract: Techniques for stain normalization image processing for digitized biological tissue images are presented. The techniques include obtaining a digitized biological tissue image; applying to at least a portion of the digitized biological tissue image an at least partially computer implemented convolutional neural network trained using a training corpus including a plurality of pairs of images, where each pair of images of the plurality of pairs of images includes a first image restricted to a lightness axis of a color space and a second image restricted to at least one of: a first color axis of the color space and a second color axis of the color space, such that the applying causes an output image to be produced; and providing the output image.

Abstract: Methods and systems for performing calculations with a neural network include determining a conductance drift coefficient for resistive processing unit (RPU) weights in a neural network. A correction factor is applied to neuron inputs in the neural network in accordance with the drift coefficient and a time that has elapsed since the RPU weights were programmed. A calculation is performed with the neural network. The correction factor compensates for conductance drift.

Abstract: A system and a method for recognition of an orchard on a geographic area are provided. The system includes a pre-processing module for deriving a target section of an aerial image containing a parcel of an orchard, an image optimization module for performing customized image processing on the target section of the aerial image, and a recognition module for determining a type and a border of the orchard present on the target section of the aerial image with a deep learning mechanism. Accordingly, farmers and agricultural entities can effectively monitor orchards within different geographic areas so as to yield better fruit production and conduct better fruit quality control and land utilization.

Abstract: The utility usage of a particular individual occupying a residence may give insight into the individual's current cognitive health and/or to enable provision of various services within the facility for the individual, particularly when monitoring patterns in utility usage over time. To enable accurate and non-invasive utility monitoring, a single-point utility sensor may be secured relative to a utility supply line, and generated signals may be utilized to monitor utility usage and to distinguish between utility fixtures. A centralized computing entity may identify frequency characteristics within the generated data, and may automatically generate one or more machine-learning algorithms to distinguish between utility usage events, without requiring substantial user input.