Abstract: There is provided a video question answering method and apparatus, an electronic device and a storage medium, which relates to the field of artificial intelligence, such as natural language processing technologies, deep learning technologies, voice recognition technologies, knowledge graph technologies, computer vision technologies, or the like. The method includes: determining M key frames for a video corresponding to a to-be-answered question, M being a positive integer greater than 1 and less than or equal to a number of video frames in the video; and determining an answer corresponding to the question according to the M key frames.

Abstract: Data processing techniques are provided. The techniques include: obtaining a first prediction data set, a model feature list and configuration information, wherein the model feature list indicates a plurality of features required by a data analysis model; generating a second prediction data set based on the model feature list and the first prediction data set, wherein the feature dimension of prediction data in the second prediction data set is smaller than the feature dimension of prediction data in the first prediction data set; performing feature transformation on a feature of the prediction data in the second prediction data set based on the configuration information to generate a third prediction data set; and inputting the third prediction data set into the data analysis model to obtain a prediction result.

Abstract: An entity recognition method and apparatus, an electronic device, a storage medium, and a computer program product are provided. The method includes: recognizing a to-be-recognized image to determine a preliminary recognition result for entities in the to-be-recognized image; determining, in response to determining that the preliminary recognition result includes a plurality of entities of a same category, image features of the to-be-recognized image and textual features of the plurality of entities; determining whether the plurality of entities is a consecutive complete entity based on the image features and the textual features, to obtain a complete-entity determining result; and obtaining a final recognition result based on the preliminary recognition result and the complete-entity determining result.

Abstract: A method for remote damage assessment of a vehicle is provided. The present disclosure relates to the technical field of artificial intelligence, in particular to the technical field of image and text recognition. An implementation solution is: performing data collection on a target vehicle to determine damage information of the target vehicle; obtaining call content of an insurance claiming call for the target vehicle, and extracting accident-related information from the call content, wherein the accident-related information includes named entities in the call content and a relationship between the named entities; and determining a first fraud probability corresponding to the target vehicle at least based on the damage information and the accident-related information.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for commenting a video, and relate to the field of cloud computing. The method may include: acquiring content information of a to-be-processed video frame; constructing text description information based on the content information, the text description information being used to describe a content of the to-be-processed video frame; importing the text description information into a pre-trained text conversion model to obtain commentary text information corresponding to the text description information, the text conversion model being used to convert the text description information into the commentary text information; and converting the commentary text information into audio information.

Abstract: An object recognition method related to the field of artificial intelligence comprises: collecting an object to be subjected to recognition (S101); according to a target text detection model corresponding to the object to be subjected to recognition, carrying out screening and recognition on full text information corresponding to the object to be subjected to recognition, so as to obtain point-of-interest text information therefrom (S102); and carrying out recognition on the point-of-interest text information according to a preset text recognition model (S103). A target text detection model obtains point-of-interest text information by means of carrying out screening and recognition on full text information, such that the recognition of full text information in the prior art is avoided, thus saving recognition time, and improving the recognition efficiency.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for commenting a video, and relate to the field of cloud computing. The method may include: acquiring content information of a to-be-processed video frame; constructing text description information based on the content information, the text description information being used to describe a content of the to-be-processed video frame; importing the text description information into a pre-trained text conversion model to obtain commentary text information corresponding to the text description information, the text conversion model being used to convert the text description information into the commentary text information; and converting the commentary text information into audio information.

Abstract: The present disclosure discloses methods, apparatuses, and electronic devices for collecting a loan, and a storage medium, relates to the field of big data technology. A method can include: acquiring information on latitude and longitude of overdue customers and longitude and debt-collectors, respectively; calculating initial paths of the debt-collectors and the overdue customers, based on the information on latitude and longitude of the overdue customers and the debt-collectors; calculating target overdue customers that the debt-collectors are responsible for and target paths of the debt-collectors and the target overdue customers that the debt-collectors are responsible for, based on the initial paths of the debt-collectors and the overdue customers; and providing the target overdue customers and the target paths to the debt-collectors, so that the debt-collectors collect loans in accordance with the target overdue customers and the target paths.