Feature interpolation techniques are described. In a training stage, features are extracted from a collection of training images and quantized into visual words. Spatial configurations of the visual words in the training images are determined and stored in a spatial configuration database. In an object detection stage, a portion of features of an image are extracted from the image and quantized into visual words. Then, a remaining portion of the features of the image are interpolated using the visual words and the spatial configurations of visual words stored in the spatial configuration database.

Technical solutions for video event recognition relate to the fields of knowledge graphs, deep learning and computer vision. A video event graph is constructed, and each event in the video event graph includes: M argument roles of the event and respective arguments of the argument roles, with M being a positive integer greater than one. For a to-be-recognized video, respective arguments of the M argument roles of a to-be-recognized event corresponding to the video are acquired. According to the arguments acquired, an event is selected from the video event graph as a recognized event corresponding to the video.

Various technologies for facilitating analysis of large remote sensing and geolocation datasets to identify features of interest are described herein. A search query can be submitted to a computing system that executes searches over a geospatial temporal semantic (GTS) graph to identify features of interest. The GTS graph comprises nodes corresponding to objects described in the remote sensing and geolocation datasets, and edges that indicate geospatial or temporal relationships between pairs of nodes in the nodes. Trajectory information is encoded in the GTS graph by the inclusion of movable nodes to facilitate searches for features of interest in the datasets relative to moving objects such as vehicles.

Techniques are described to generate improved training data for pixel labeling. To generate training data, objects are displayed in a user interface by a computing device, e.g., iteratively. The objects are taken from a structured object representation associated with a respective one of a plurality of images. The structured object representation defines a hierarchical relationship of the objects within the respective image. Inputs are then received that are originated through user interaction with the user interface. The inputs label respective ones of the iteratively displayed objects, e.g., as text, a graphical element, background, foreground, and so forth. A model is trained by the computing device using machine learning.

Systems and methods for segmenting scan data are disclosed. The methods include creating a graph from scan data representing a plurality of points in an environment associated with a ground and one or more objects, where the graph includes a plurality of vertices corresponding to the plurality of points in the environment, a first terminal vertex associated with the ground label, and a second terminal vertex associated with the non-ground label. A unary potential being the cost of assigning a vertex to a ground label or a non-ground label is assigned to each vertex, and a pairwise potential is assigned to each pair of neighboring vertices in the graph as a measure of a cost of assigning different labels. The methods include using the unary the pairwise potentials to identify labels for each point and segmenting the scan data to identify points associated with the ground.

A computational systems pathology spatial analysis platform includes: (i) a spatial heterogeneity quantification component configured for generating a global quantification of spatial heterogeneity among cells of varying phenotypes in multi-parameter cellular and subcellular imaging data; (ii) a microdomain identification component configured for identifying a plurality of microdomains for tissue samples based on the global quantification, each microdomain being associated with a a tissue sample; and (iii) a weighted graph component configured for constructing a weighted graph for the multi-parameter cellular and subcellular imaging data, the weighted graph having a plurality of nodes and a plurality of edges each being located between a pair of the nodes, wherein in the weighted graph each node is a particular one of the microdomains and the edge between each pair of microdomains in the weighted graph is indicative of a degree of similarity between the pair of the microdomains.

The invention relates to a method and a system for preparing data on dynamic spatial scenarios, to a computer-supported method, to a system for training artificial neural networks, to a computer-supported method, and to a system for analyzing sensor data. A display of a time curve of an angular sector covered by another object from the perspective of an ego object is generated. The time curve is ascertained from sensor data, and the sensor data characterize a dynamic spatial scenario with respect to the ego object and at least one other object.

A computer-implemented method for computing a pathological condition of a subject, comprising obtaining (10) initial cranial image data of a subject from an input interface, and incorporating the initial cranial image data into a knowledge model comprised within a semantic network stored in a memory performing (12), via a processor, at least one processing sequence on the initial cranial image data using the semantic network to thus provide, in the semantic network, at least one element comprising topographical data of the subject's brain, or a portion of the subject's brain, referenced to a reference coordinate system wherein the at least one processing sequence performs at least one state iteration of at least a portion of the semantic network from a first state into a second state comparing (14) the topographical data of the subject's brain to one, or more pathological condition prediction elements of the semantic network to form an indication of a pathological condition of the subject, and generating (16) an additional element in the semantic network comprising the indication of the pathological condition of the subject.

A computer-implemented method for computing a pathological condition of a subject, comprising obtaining (10) initial cranial image data of a subject from an input interface, and incorporating the initial cranial image data into a knowledge model comprised within a semantic network stored in a memory performing (12), via a processor, at least one processing sequence on the initial cranial image data using the semantic network to thus provide, in the semantic network, at least one element comprising topographical data of the subject's brain, or a portion of the subject's brain, referenced to a reference coordinate system wherein the at least one processing sequence performs at least one state iteration of at least a portion of the semantic network from a first state into a second state comparing (14) the topographical data of the subject's brain to one, or more pathological condition prediction elements of the semantic network to form an indication of a pathological condition of the subject, and generating (16) an additional element in the semantic network comprising the indication of the pathological condition of the subject.

The present disclosure provides a method for visual question answering. The method includes: acquiring an input image and an input question; constructing a visual graph based on the input image, wherein the visual graph comprises a first node feature and a first edge feature; constructing a question graph based on the input question, wherein the question graph comprises a second node feature and a second edge feature; performing a multimodal fusion on the visual graph and the question graph to obtain an updated visual graph and an updated question graph; determining a question feature based on the input question; determining a fusion feature based on the updated visual graph, the updated question graph and the question feature; and generating a predicted answer for the input image and the input question. The present disclosure further provides an apparatus for visual question answering, a computer device and a medium.