A device includes a memory configured to store instructions. The device also includes one or more processors configured to execute the instructions to provide context and one or more items of interest corresponding to the context to a dependency network encoder to generate a semantic-based representation of the context. The one or more processors are also configured to provide the context to a data dependent encoder to generate a context-based representation. The one or more processors are further configured to combine the semantic-based representation and the context-based representation to generate a semantically-augmented representation of the context.

Systems and methods for implementing a threat model that classifies contextual events as threats.

A method, apparatus and system for zero shot object detection includes, in a semantic embedding space having embedded object class labels, training the space by embedding extracted features of bounding boxes and object class labels of labeled bounding boxes of known object classes into the space, determining regions in an image having unknown object classes on which to perform object detection as proposed bounding boxes, extracting features of the proposed bounding boxes, projecting the extracted features of the proposed bounding boxes into the space, computing a similarity measure between the projected features of the proposed bounding boxes and the embedded, extracted features of the bounding boxes of the known object classes in the space, and predicting an object class label for proposed bounding boxes by determining a nearest embedded object class label to the projected features of the proposed bounding boxes in the space based on the similarity measures.

In one implementation, a method of generating a plane hypothesis is performed by a device including one or more processors, non-transitory memory, and a scene camera. The method includes obtaining an image of a scene including a plurality of pixels. The method includes obtaining a plurality of points of a point cloud based on the image of the scene. The method includes obtaining an object classification set based on the image of the scene. Each element of the object classification set includes a plurality of pixels respectively associated with a corresponding object in the scene. The method includes detecting a plane within the scene by identifying a subset of the plurality of points of the point cloud that correspond to a particular element of the object classification set.

A method for cutting or extracting video clips from a video, including the audio content relevant to points of particular interest, and combining the same for instruction or training on particular points; a computing device applying the method extracts text information from the spoken audio content of a video to be cut and obtains multiple paragraph segmentation positions as candidates for inclusion in a desired and finished presentation by analyzing the information from text representing the spoken audio content, the analysis being carried out by a semantic segmentation model. Candidate items of text are obtained by isolating pieces of text according to the paragraph segmentation positions. Time stamps of the candidate text segments are acquired, and candidate video clips are obtained by cutting the video according to the acquired time stamps.

The disclosure relates to a method for determining a boundary about an area of interest in an image set. The includes obtaining the image set from an imaging modality and processing the image set in a convolutional neural network. The convolutional neural network is trained to perform the acts of predicting an inverse distance map for the actual boundary in the image set; and deriving the boundary from the inverse distance map. The disclosure also relates to a method of training a convolutional neural network for use in such a method, and a medical imaging arrangement.

The invention relates to a method implemented by a computing device for processing math and text in handwriting, comprising: identifying symbols by performing handwriting recognition on a plurality of strokes; classifying, as a first classification, first symbols as either a text symbol candidate or a math symbol candidate with a confidence score reaching a first threshold; classifying, as a second classification, second symbols other than first symbols as either a text symbol candidate or a math symbol candidate with a respective confidence score by applying predefined spatial syntactic rules; updating or confirming, as a third classification, a result of the second classification by establishing semantic connections between symbols and comparing the semantic connections with the result of the second classification; and recognising each symbol as either text or math based on a result of said third classification.

The present teaching relates to method, system, and programming for responding to an image related query. Information related to each of a plurality of images is received, wherein the information represents concepts co-existing in the image. Visual semantics for each of the plurality of images are created based on the information related thereto. Representations of scenes of the plurality of images are obtained via machine learning, based on the visual semantics of the plurality of images, wherein the representations capture concepts associated with the scenes.

One embodiment of a method includes applying a first generator model to a semantic representation of an image to generate an affine transformation, where the affine transformation represents a bounding box associated with at least one region within the image. The method further includes applying a second generator model to the affine transformation and the semantic representation to generate a shape of an object. The method further includes inserting the object into the image based on the bounding box and the shape.

A non-transitory computer-readable storage medium storing an output program that causes at least one computer to execute a process, the process includes converting input data into a semantic representation; and outputting a validity score based on a matching degree between a first relationship between a noun and a verb in the semantic representation and a second relationship between the noun and the verb in a database.