Various implementations of the present disclosure relate to style transfer. In some implementations, a computer-implemented method comprises: obtaining a target object having a first style, a style of the target object being editable; obtaining a reference image including a reference object; obtaining a second style of the reference object, the second style of the reference object being extracted from the reference image; and applying the second style to the target object.

Various implementations of the present disclosure relate to style transfer. In some implementations, a computer-implemented method comprises: obtaining a target object having a first style, a style of the target object being editable; obtaining a reference image including a reference object; obtaining a second style of the reference object, the second style of the reference object being extracted from the reference image; and applying the second style to the target object.

A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

A method of extracting a table information, an electronic device, and a storage medium are provided, which relate to fields of artificial intelligence and big data, in particular to fields of machine learning, knowledge graph, intelligent search and intelligent recommendation, and may be used for an intelligent extraction of an information in a table and other scenarios. The method includes: performing a clustering based on features of a plurality of rows of cells and/or features of a plurality of columns of cells in a table, so as to determine candidate header cells in the table; and performing an information extraction on the table based on the candidate header cells, so as to extract attribute-attribute value pairs in the table.

Various embodiments provided herein relate to a method and system for automatic formatting of presentation slides. In at least one embodiment, the method comprises receiving an input slide, the input slide comprising one or more objects having a first slide layout configuration; determining metadata associated with the input slide, the determined metadata corresponding to property features of the one or more objects; analyzing the metadata to classify the one or more objects; identifying one or more slide layout functional objectives; based on the one or more slide layout functional objectives, applying one or more transformations to the detected objects, wherein each transformation comprises adjusting the metadata corresponding to the one or more detected objects to generate one or more adjusted objects; and generating a modified slide, the modified slide comprising one or more adjusted objects having a second slide layout configuration.

Systems and methods to facilitate a customization and proliferation of models are described. The system receives, via a first interface, table information and communicates the table information to a first model. The first model includes logic to process the values to generate a column of predicted values. The system receives a column of predicted values from the first model. The system appends the column of predicted values to the table information to generate appended table information. The system communicates, via a second interface, the appended table information to a second state including a second plurality of models. The sequence of states is associated with a plurality of interfaces including the first interface and the second interface. The interfaces facilitate a customization and proliferation of models.

Systems and methods to facilitate a customization and proliferation of models are described. The system receives, via a first interface, table information and communicates the table information to a first model. The first model includes logic to process the values to generate a column of predicted values. The system receives a column of predicted values from the first model. The system appends the column of predicted values to the table information to generate appended table information. The system communicates, via a second interface, the appended table information to a second state including a second plurality of models. The sequence of states is associated with a plurality of interfaces including the first interface and the second interface. The interfaces facilitate a customization and proliferation of models.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Implementations of the subject matter described herein provide a solution for generating a text from the structured data. In this solution, the structured data is converted into its representation, where the structured data comprises a plurality of cells, and the representation of the structured data comprises plurality of representations of the plurality of cells. A natural language sentence associated with the structured data may be determined based on the representation of the structured data, thereby implementing the function of converting the structured data into a text.