A model learning device provided with: an error-added movement locus generation unit for adding an error to movement locus data for action learning that represents the movement locus of a subject and to which is assigned an action label that is information representing the action of the subject, and thereby generating error-added movement locus data; and an action recognition model learning unit for learning a model, using at least the error-added movement locus data and learning data created on the basis of the action label, by which model the action of some subject can be recognized from the movement locus of the subject. Thus, it is possible to provide a model by which the action of a subject can be recognized with high accuracy on the basis of the movement locus of the subject estimated using a camera image.

Systems and methods are disclosed to objectively identify sub-second behavioral modules in the three-dimensional (3D) video data that represents the motion of a subject. Defining behavioral modules based upon structure in the 3D video data itself—rather than using a priori definitions for what should constitute a measurable unit of action—identifies a previously-unexplored sub-second regularity that defines a timescale upon which behavior is organized, yields important information about the components and structure of behavior, offers insight into the nature of behavioral change in the subject, and enables objective discovery of subtle alterations in patterned action. The systems and methods of the invention can be applied to drug or gene therapy classification, drug or gene therapy screening, disease study including early detection of the onset of a disease, toxicology research, side-effect study, learning and memory process study, anxiety study, and analysis in consumer behavior.

Described is a system for detecting and correcting perception errors in a perception system. In operation, the system generates a list of detected objects from perception data of a scene, which allows for the generation of a list of background classes from backgrounds in the perception data associated with the list of detected objects. For each detected object in the list of detected objects, a closest background class is identified from the list of background classes. Vectors can then be used to determine a semantic feature, which is used to identify axioms. An optimal perception parameter is then generated, which is used to adjust perception parameters in the perception system to minimize perception errors.

The disclosure relates to systems and methods for evaluating a blood vessel. The method includes receiving image data of the blood vessel acquired by an image acquisition device, and predicting, by a processor, blood vessel condition parameters of the blood vessel by applying a deep learning model to the acquired image data of the blood vessel. The deep learning model maps a sequence of image patches on the blood vessel to blood vessel condition parameters on the blood vessel, where in the mapping the entire sequence of image patches contribute to the blood vessel condition parameters. The method further includes providing the blood vessel condition parameters of the blood vessel for evaluating the blood vessel.

Different from prior works that model the internal distribution of patches within an image implicitly with a top-down latent variable model (e.g., generator), embodiments explicitly represent the statistical distribution within a single image by using an energy-based generative framework, where a pyramid of energy functions, each parameterized by a bottom-up deep neural network, are used to capture the distributions of patches at different resolutions. Also, embodiments of a coarse-to-fine sequential training and sampling strategy are presented to train the model efficiently. Besides learning to generate random samples from white noise, embodiments can learn in parallel with a self-supervised task (e.g., recover an input image from its corrupted version), which can further improve the descriptive power of the learned model. Embodiments does not require an auxiliary model (e.g., discriminator) to assist the training, and embodiments also unify internal statistics learning and image generation in a single framework.

A computerized device for driving assistance comprises a memory (4) designed to receive data point cloud data (8) in which a point cloud associates, for a given instant, points each having coordinates in a plane associated with the point cloud and a value denoting a height. The device furthermore comprises a calculator (6) designed to access the memory (4) and, for a given point cloud, to calculate data on the probability of belonging to a reference surface, associated with each point of the data point cloud, on the one hand, and node data associating a value denoting a height (hi) and two values indicating a slope in a plane associated with the plane of the given point cloud, on the other hand, by determining a Gaussian random conditional field by way of the data point cloud data (8) corresponding to the given point cloud, which Gaussian random conditional field is represented by a mesh of nodes in said associated plane, which nodes are defined by the node data, and to return the data on the probability of belonging to a reference surface and/or at least some of the node data and values denoting a height.

Techniques for cognitive analysis for directed control transfer with autonomous vehicles are described. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.

In some embodiments, a method includes training a first machine learning model based on multiple documents and multiple templates associated with the multiple documents. The method further includes executing the first machine learning model to generate multiple relevancy masks, the multiple relevancy masks to remove a visual structure of the multiple templates from a visual structure of the multiple documents. The method further includes generating multiple multichannel field images to include the multiple relevancy masks and at least one of the multiple documents or the multiple templates. The method further includes training a second machine learning model based on the multiple multichannel field images and multiple non-native texts associated with the multiple documents. The method further includes executing the second machine learning model to generate multiple non-native texts from the multiple multichannel field images.

In one embodiment, a method includes accessing a first set of images of multiple images of a scene, wherein the first set of images show the scene during a time period. The method includes generating, by processing the first set of images using a first machine-learning model, one or more attributes representing observed actions performed in the scene during the time period. The method includes predicting, by processing the generated one or more attributes using a second machine-learning model, one or more actions that would happen in the scene after the time period.

The present disclosure provides a dance animation processing method and apparatus, an electronic device, and a storage medium. The method includes: acquiring multiple dance action segments, and establishing an animation state transition relationship for the multiple dance action segments, each action node in the animation state transition relationship corresponding to one dance action segment, and a transition cost existing among the action nodes; acquiring a target audio file, and determining a music feature sequence for the target audio file; determining a dance action sequence for the music feature sequence according to the transition cost in the animation state transition relationship; and generating a dance animation for the target audio file according to the dance action sequence.