Abstract: A method for predicting a structure of a protein complex includes: obtaining an initial coordinate of each amino acid residue in a target protein complex, and obtaining a target residue pair feature, a first multiple sequence alignment (MSA) feature and a second MSA feature of each protein monomer in the target protein complex; and inputting the initial coordinate of each amino acid residue, and the target residue pair feature, the first MSA feature and the second MSA feature of each protein monomer into an N-level fold iteration network layer, and obtaining a target coordinate of each amino acid residue by predicting a torsion angle, a position transformation at residue level and a position transformation at monomer chain level of each amino acid residue via the N level fold iteration network layer, to obtain a predicted structure of the protein complex.

Abstract: The disclosure provides a method for training a semantic retrieval network, an electronic device and a storage medium. The method includes: obtaining a training sample including a search term and n candidate files corresponding to the search term, where n is an integer greater than 1; inputting the training sample into the ranking model, to obtain n first correlation degrees output by the ranking model, in which each first correlation degree represents a correlation between a candidate document and the search term; inputting the training sample into the semantic retrieval model, to obtain n second correlation degrees output by the semantic retrieval model, wherein each second correlation degree represents a correlation between a candidate document and the search term; and training the semantic retrieval model and the ranking model jointly based on the n first correlation degrees and the n second correlation degrees.

Abstract: A method for processing a model operator includes: determining an operator set for model networking, wherein the operator set comprises a plurality of operators; determining a storage amount occupied by an output tensor of each operator in the operator set and a computation time period consumed in a forward computation of each operator in the operator set; and determining a first operator participating in recomputation in a model from the operator set, based on the storage amounts and the computation time periods of the plurality of operators.

Abstract: A method for determining multimedia editing information includes: obtaining a first playback time of a first target frame in first multimedia data to be edited, a second playback time of the first target frame for second multimedia data after editing and a playback multi-times speed set for the first multimedia data; determining, based on the first playback time and the second playback time, a time deviation that occurs while editing the first multimedia data at the playback multi-times speed for the first multimedia data; and determining, based on the time deviation, the first playback time, the second playback time and the playback multi-times speed, a playback time of the first multimedia data corresponding to each playback time of the second multimedia data after the second playback time.

Abstract: The disclosure provides a code completion method based on a big model. The method includes: determining a first code element where a position to be completed is located in a first code file to be completed; determining a second code file having a dependency relationship with the first code file from a development project to which the first code file belongs; determining, according to the first code element, a second code element whose correlation with the first code element meets a preset condition, in which the second code element belongs to at least one of the first code file or the second code file; and generating a target code corresponding to the position to be completed through a big model based on a signature of the second code element.

Abstract: The present disclosure provides method and apparatus for transferring facial expression of digital human, electronic device, and storage medium, which relates to the fields of augmented reality technologies, virtual reality technologies, computer vision technologies, deep learning technologies, or the like, and can be applied to scenarios, such as metaverse, a virtual digital human, or the like, An implementation includes: screening an identification of a target reference model matched with an object model from a preset reference model library; the reference model library including a plurality of reference models; acquiring an expression library of the target reference model based on the identification of the target reference model; and transferring a last frame of an expression in the expression library of the target reference model into the object model to obtain a last frame of an expression of the object model.

Abstract: The present disclosure provides method and apparatus for generating 3D scene based on large language model, electronic device, and storage medium, which relates to the field of artificial intelligence technologies, particularly the fields of three-dimensional modeling technologies, large language model technologies, or the like. The three-dimensional scene generating method based on a large language model includes: processing description information of a target three-dimensional scene to obtain label information in the description information; generating query operation prompt of the LLM based on the label information, and acquiring a target asset set matched with the label information by the LLM based on the query operation prompt, the target asset set including a target asset in the target three-dimensional scene, target material information of the target asset and target scene attribute information of the target asset; and generating the target three-dimensional scene based on the target asset set.

Abstract: A method for image processing, including: obtaining an image to be processed; determining a portrait area in the image to be processed, and cropping, based on the portrait area, a target background image from the image to be processed; obtaining a portrait by performing portrait matting on the image to be processed and obtaining an enlarged portrait by enlarging the portrait, wherein a height of the enlarged portrait is greater than a height of the target background image; and generating, based on the target background image and the enlarged portrait, a target image.

Abstract: A digital human generation method, an electronic device and a storage medium are disclosed. The solution relates to the fields of augmented reality technologies, virtual reality technologies, computer vision technologies, deep learning technologies, or the like, and can be applied to scenarios, such as metaverse, a virtual digital human, or the like. An implementation includes: acquiring a corresponding target object model based on a picture of a to-be-generated digital human; acquiring a corresponding point cloud of a head key feature in the picture from a pre-configured feature library based on the head key feature; and fusing the point cloud of the head key feature in the target object model to obtain a digital human figure.

Abstract: There is provided a method for updating information of a point of interest, an electronic device and a storage medium, relating to the field of artificial intelligence, in particular to the field of information intelligent search, and being applicable to a scene for identifying a specified type of text content. The method includes identifying a user focus information to be approved for a target point of interest from at least one user-generated content; constructing an approval request information for approving the user focus information; pushing the approval request information to an operator of the target point of interest and obtaining an approval result information for the user focus information from the operator; and updating an attribute information of the target point of interest according to the approval result information.

Abstract: A text processing method is provided. The method includes: a first probability value of each candidate character of a plurality of candidate characters corresponding to a target position is determined based on character feature information corresponding to the target position in a text fragment to be processed, wherein the character feature information is determined based on a context at the target position in the text fragment to be processed; a second probability value of each candidate character of the plurality of candidate characters is determined based on a character string including the candidate character and at least one character in at least one position in the text fragment to be processed adjacent to the target position; and a correction character at the target position is determined based on the first probability value and the second probability value of each candidate character of the plurality of candidate characters.

Abstract: A method of training a model, a method of determining a word vector, a device, a medium, and a product are provided, which may be applied to fields of natural language processing, information processing, etc. The method includes: acquiring a first word vector set corresponding to a first word set; and generating a reduced-dimensional word vector for each word vector in the first word vector set based on a word embedding model, generating, for other word vector in the first word vector set, a first probability distribution in the first word vector set based on the reduced-dimensional word vector, and adjusting a parameter of the word embedding model so as to minimize a difference between the first probability distribution and a second probability distribution for the other word vector determined by a number of word vector in the first word vector set.

Abstract: A method for processing map data includes: determining a first region for which a map display image is to be drawn; determining multiple first reference points within the first region according to a second region corresponding to the first region; determining relative height data of the first region according to positions and real height data of all the first reference points; and determining a map display image of a position to be displayed according to the relative height data, in which the position to be displayed is within the first region.

Abstract: The present disclosure discloses a method and apparatus for acquiring a pre-trained model, and relates to natural language processing and deep learning technologies in the field of artificial intelligence technologies. An implementation includes: acquiring training data, the training data including a single-modal language material and a multi-modal language material, and the multi-modal language material including a language material pair formed by a first-modal language material and a second-modal language material; and performing a multi-task training operation on a pre-trained model using the training data, the multi-task including at least one cross-modal contrastive learning task and at least one single-modal learning task; the pre-trained language model obtained in the present disclosure may learn from different forms of language materials, i.e.

Abstract: A computer-implemented method for information processing includes: obtaining text information, in which the text information includes first text information of a resource to be commented on and second text information of a candidate prompt; selecting a target prompt from the candidate prompts based on the text information; and generating comment information of the resource to be commented on, based on the resource to be commented on and the target prompt.

Abstract: A method for model training based on a large model includes: determining a first large model as a teacher model of a language model, and performing distillation learning on the language model based on the first large model; inputting a first prompt text into the language model, and obtaining a plurality of first response texts for the first prompt text output by the language model; determining a reference response text for the first prompt text from the plurality of first response texts; and training the language model based on the reference response text for the first prompt text.

Abstract: A large model-based recommendation method includes: determining description information of interested content corresponding to a target user; inputting a content to be recommended, the description information of interested content and current popular search sentences into a large model to generate at least one recommendation card corresponding to the content to be recommended, in which the recommendation card contains a recommendation word associated with the content to be recommended; obtaining a current behavior characteristic of the target user; and in response to the current behavior characteristic satisfying a display condition of the recommendation card, displaying the recommendation card corresponding to at least one content to be recommended.

Abstract: A method for obtaining a cover image includes: obtaining a plurality of first cropped images of an original image corresponding to a candidate resource; obtaining an aesthetic score of each of the plurality of first cropped images; and determining a target cover image of the candidate resource from the plurality of first cropped images based on the aesthetic score of each first cropped image.

Abstract: The disclosure provides a method and an apparatus for generating comment information based on a large model, an electronic device and a storage medium, relates to a technical field of artificial intelligence, and in particular to the technical fields of deep learning, large model, and natural language processing, and the like. The specific technical solution includes: obtaining description information of a resource to be commented on by understanding, based on the large model, the resource to be commented on; obtaining, based on the description information, comment information of the resource to be commented on, in which the comment information includes at least a comment video of the resource to be commented on; and displaying the comment video in a comment section. The intelligent generation of comment videos and texts is realized, improving the accuracy of the comment information, simplifying the comment generation process, and improving the speed of generating comments.

Abstract: The present disclosure provides a method and apparatus for a route navigation, which relate to the technical fields of computer vision, image processing, augmented reality and the like. An implementation solution is: performing an augmented reality navigation on a current route based on real-time acquired GPS information of a terminal; acquiring SLAM (simultaneous localization and mapping) coordinates of the terminal; converting, in response to determining that the GPS information meets a preset positioning difference condition, the SLAM coordinates into calculated geographic coordinates; and performing an augmented reality navigation on the current route based on the calculated geographic coordinates.