Abstract: The present disclosure describes a model for large scale color prediction of objects identified in images. Embodiments of the present disclosure include an object detection network, an attention network, and a color classification network. The object detection network generates object features for an object in an image and may include a convolutional neural network (CNN), region proposal network, or a ResNet. The attention network generates an attention vector for the object based on the object features, wherein the attention network takes a query vector based on the object features, and a plurality of key vector and a plurality of value vectors corresponding to a plurality of colors as input. The color classification network generates a color attribute vector based on the attention vector, wherein the color attribute vector indicates a probability of the object including each of the plurality of colors.

Abstract: System and method for adjusting side mirrors capture, by a primary camera, a facial image of a driver; estimate a 3D eye location with respect to a 3D coordinate of the primary camera based on the captured facial image; map the estimated 3D eye location to a respective set of pitch and yaw of the side mirror based on a look up table; and rotate the side mirror to a target position in accordance with the respective set of pitch and yaw. The lookup table includes a plurality of rows, each containing a pair of a key and a set of values. The key is a set of three parameters of 3D line of locations where the driver can observe an optimal view behind the vehicle, and the set of values includes a set of pitch and yaw of the side mirror corresponding to the 3D line of locations.

Abstract: A system for generating an advanced surround view for a vehicle includes a plurality of cameras to generate image data. Sensors are configured to determine kinematic data of the vehicle. Kinematic data are also obtained by a visual odometry module and/or a deep-vision mechanism. A processor is configured to process image data captured by the plurality of cameras to generate a 360-degree surround view layer representative of a surrounding area. The processor is further configured to construct an improved bicycle kinematic model for processing kinematic data to generate an under-chassis layer representative of an area under the vehicle. The processor is further configured to generate a 3D vehicle model layer and overlay objects layer. Display screens display a GUI of combined scene of the 360-degree surround view layer, under-chassis layer, 3D vehicle model layer and overlay objects layer as would be viewed from a virtual camera viewpoint.

Abstract: A method for adjusting a side mirror of a vehicle includes capturing, by a primary camera provided in front of a driver of the vehicle, a facial image of the driver; estimating a 3D eye location with respect to a 3D coordinate of the primary camera based on the captured facial image; mapping the estimated 3D eye location to a respective set of pitch and yaw angles of the side mirror based on a lookup table; and rotating the side mirror to a target position in accordance with the respective set of pitch and yaw angles. The lookup table includes a plurality of rows, each row containing a pair of a key and a set of values. The key is a 3D eye location, and the set of values includes a set of pitch and yaw angles of the side mirror corresponding to the 3D eye location.

Abstract: Method of analysis text message syntactically and by content, which entails: step 1; Split syntaxes (made available to subscribers by the network operator) into tokens to store in a Syntax Trie; step 2. Pre-process an incoming text from a subscriber; step 3. Split the text (pre-processed in Step 2) into tokens; step 4. Look up paths that include the tokens (obtained in Step 3) in the Syntax Trie (initialized in Step 1); step 5: Return the look-up result, which is the path in the Syntax Trie that best reflects the user intent.

Abstract: The present invention relates to a method and a system for training a machine learning model for face recognition. The method comprising generating a training dataset; providing a teacher model that comprises a teacher backbone and a teacher head; iteratively training the teacher backbone and the teacher head using the training dataset; setting the machine learning model to include a lightweight backbone and a lightweight head; copying trained parameters of the trained teacher head to the lightweight head; and iteratively training the lightweight backbone using the training dataset.

Abstract: The present description relates to systems, methods, and non-transitory computer readable media for generating digital responses to digital queries by utilizing a classification model and query-specific analysis models. For example, the described systems can train a classification model to generate query classifications corresponding to product queries, conversational queries, and/or recommendation/purchase queries. Moreover, the described systems can apply the classification model to select pertinent models for particular queries. For example, upon classifying a product query, the described systems can utilize a neural ranking model (trained based on a set of training product specifications and training queries) to generate relevance scores for product specifications associated with a digital query.

Abstract: A method and system for a base station (BS) configured for sharing among a plurality of public land mobile networks (PLMNs), and for providing open radio access and/or closed subscriber group (CSG) radio access for user equipment devices (UEs) served. The BS may make a determination of which PLMNs have enabled CSG and which have not, and may transmit a request to a core network of each PLMN to set up an interface connection with each. Based on the determination, the request may either include information for configuring CSG radio access, or information for configuring only open radio access. For each PLMN having CSG enabled, the interface connection may be established for CSG radio access for UEs associated with PLMNs having CSG enabled. For each PLMN not having CSG enabled, the interface connection may be established for only open radio access for UEs associated with PLMNs without CSG enabled.

Abstract: Maneuver assisting system and method are provided. The system includes at least one processor configured to process operations of the system; one or more memories for storing instructions; a communication unit for communicating between components of the system, and between the system and the vehicle and/or a user equipment; a Surrounding View Monitoring (SVM) unit comprising a plurality of sensors for providing a plurality of vehicle-related information; a Human-Machine Interface (HMI) configured for a driver of the vehicle to interact with the system; a display unit configured to display the HMI; a motion planning unit configured to generate a trajectory for the vehicle to follow; and a motion control unit configured to control the automated maneuvers of the vehicle. In particular, the HMI is configured to be implemented in the user equipment in order for the driver to remotely operate the system using the user equipment.

Abstract: A method and system for training a machine learning model for point of gaze prediction simultaneously analyzes the image of the scene and the image of the driver's face using the machine learning model to generate a point of gaze of a driver in traffic environments. The machine learning model comprises a camera calibration module that can compute an embedding vector that represents the spatial configuration between the driver and the camera system. The camera calibration module improves the overall model's performance, which is jointly trained end to end.

Abstract: A method of clustering using encoder-decoder model based on attention mechanism extracts image features, clusters to form image feature vector clusters, and based on the cosine similarity score between the image feature vectors to arrange each image feature vector cluster into an image feature vector sequence. The image feature vector sequence includes cosine distance encoding vectors concatenated with respective image feature vectors and is used as the input data sequence in encoder and decoder neural network models to generate an output data sequence from the input data sequence. The output data sequence is a binary sequence having values of 1 or 0 at a position denoting that the image corresponding to the position is or is not in the same cluster with respect to the center image of the cluster.

Abstract: A parking slot detection method and system includes receiving a plurality of images taken from a plurality of cameras mounted on a vehicle in a parking environment; generating a top view image comprising a surrounding view of the vehicle based on the plurality of images; processing the top view image using a parking line detection model that has been trained using an annotated dataset to detect parking lines for a parking slot in the parking environment, estimate a bounding box for the parking slot and identify an occupancy state of the parking slot; and converting pixel coordinate information of the bounding box to vehicle information.

Abstract: In a method and apparatus for extreme-light image enhancement, an input image is passed to a lighting classification module in order to be classified into two categories: an under-exposed lighting condition and an over-exposed light condition. Then the classified image is subsequently passed to an enhancement module which comprises a first image enhancement unit and a second image enhancement unit using a mapping function based on a gamma mapping function for mapping the classified image to an output image, respectively. A parameter of the gamma mapping function is estimated by a parameter estimation network, based on the classified image. The parameter estimation network may be a lightweight CNN network which is trained by an unsupervised approach.

Abstract: A driver monitor system includes an image data acquiring module configured to acquire a plurality of image data from a data collection module; a training module configured to train a plurality of teacher models to obtain a plurality of feature groups using the plurality of image data, and transfer a plurality of pieces of knowledge obtained from the plurality of feature groups to a plurality of student models, respectively; and at least one edge device comprising the plurality of student models configured to use a pipeline design pattern with multiple threads to make a warning.

Abstract: Techniques disclosed herein provide more efficient and more relevant item recommendations to users in large-scale environments in which only positive interest information is known. The techniques use a rank-constrained formulation that generalizes relationships based on known user interests in items and/or use a randomized singular value decomposition (SVD) approximation technique to solve the formulation to identify items of interest to users in an efficiently, scalable manner.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.

Abstract: Systems and methods are disclosed herein for providing a user interface representing differences between segments of end users. The systems and methods receive user input on a user interface identifying a first segment, the first segment being a subset of the end users having a particular characteristic, determine differences between the first segment and a second segment, and represent, on the user interface, the differences between the first segment and the second segment based on relative significances of the differences. The marketer using the user interface is able to quickly and easily identify the metrics, dimensions, and/or relationships to other segments that most distinguish the compared segments from one another.

Abstract: Certain embodiments involve determining and outputting correlations between metrics in large-scale web analytics datasets. For example, a processor identifies pairs of data metrics in a web analytics data set and determines a Maximal Information Coefficient (MIC) score for each pair of data metrics that indicates a strength of a correlation between the pair of data metrics. The processor generates an interactive user interface that graphically displays each pair of correlated data metrics having an MIC score above a threshold and the interactive user interface indicates the strength of the correlation between each displayed pair of correlated data metrics. The processor receives user input indicating an adjustment to the threshold and modifies the interactive user interface in response to receiving the user input by adding pairs of correlated data metrics to, or removing pairs of correlated metrics from, the interactive user interface based on the adjustment to the threshold.

Abstract: Systems and methods of operating a wireless communication system are provided. A network node can receive a request from the wireless device to establish communication with the first access node. A network node can receive an indication from an authorization node that a wireless device is not authorized to communicate with a first access node. The network node can transmit a message denying the request to establish communication with the first access node to the wireless device based on the indication from the authorization node. The network node can receive a request from the wireless device to establish communication with a second access node. The network node can determine that the wireless device is authorized to establish communication with the second access node. The network node can transmit a message granting the request to establish communication with the second access node to the wireless device.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating dialogue responses based on received utterances utilizing an independent gate context-dependent additive recurrent neural network. For example, the disclosed systems can utilize a neural network model to generate a dialogue history vector based on received utterances and can use the dialogue history vector to generate a dialogue response. The independent gate context-dependent additive recurrent neural network can remove local context to reduce computation complexity and allow for gates at all time steps to be computed in parallel. The independent gate context-dependent additive recurrent neural network maintains the sequential nature of a recurrent neural network using the hidden vector output.