Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for verifying a virtual avatar. The method includes: determining, based on the virtual avatar and a plurality of reference images in an image library, a first similarity set comprising a plurality of similarities between the virtual avatar and the plurality of reference images; and selecting a plurality of candidate images from the plurality of reference images based on the first similarity set and a predetermined number. The method further includes: determining a second similarity set comprising a plurality of similarities between the virtual avatar and the plurality of candidate images; and determining a verification result for the virtual avatar based on a subset in the first similarity set that is associated with the plurality of candidate images, and the second similarity set.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for rendering. The method includes obtaining features of a three-dimensional (3D) graph based on the 3D graph and a pose of a camera capturing the 3D graph. The method further includes generating a plurality of two-dimensional (2D) subgraphs for the 3D graph based on the features of the 3D graph, wherein the plurality of 2D subgraphs are adjustable. The method further includes rendering a scene in the 3D graph based on the generated plurality of 2D subgraphs. In this way, customization of a rendering process can be achieved, and visual quality during scene decomposition and synthesis can be improved, thereby enhancing 3D reconstruction.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing a target object. The method includes acquiring an initial non-video feature vector on the basis of at least one input of a received speech input and text input. The method further includes taking, in response to not receiving a video input, a default feature vector as an initial video feature vector corresponding to the video input. The method further includes generating a video feature, a speech feature, and a text feature on the basis of the initial non-video feature vector and the initial video feature vector. The method further includes generating a processing parameter for a target object on the basis of the video feature, the speech feature, and the text feature, wherein the processing parameter includes at least one of an emotion parameter, an attribute parameter, and a pose parameter.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for virtual reality modeling. The method includes obtaining a first image of a real object at a first viewing angle. The method further includes obtaining, based on the first image, an initial three-dimensional model corresponding to the real object. The method further includes determining a second image of the real object at a second viewing angle different from the first viewing angle by using the initial three-dimensional model. The second image has texture characteristics of the real object. The method further includes generating a target three-dimensional model used for virtual reality and corresponding to the real object by using the second image and the first image. Through the solution, a three-dimensional model which can reproduce a real object with high quality may be obtained based on a small number of input images.

Abstract: Embodiments of the present disclosure relate to an article processing method, electronic device, and computer program product. The method includes: determining, based on content of a target article, a target article vector associated with the target article; acquiring a reference article vector set associated with a reference article set; and determining, based on a distance in an article vector space between the target article vector and a reference article vector in the reference article vector set, a reference article vector associated with the target article vector in the reference article vector set as an association article vector. By using the technical solution of the present disclosure, an association article associated with a target article can be accurately provided based on the target article selected by a user, so that reports on the target article and its association articles can be further provided to the user for analysis and selection.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for session switching. The method includes: establishing a first session with a first terminal in response to a session request initiated by the first terminal. The method further includes: generating first response information to first request information from the first terminal. The method further includes: determining a first confidence level of the first response information; and determining, in response to the first confidence level being lower than a confidence level threshold, to switch the first session to a second session between the first terminal and a second terminal. The method for session switching according to embodiments of the present disclosure can provide a fast and satisfactory response to a request made by a user, thereby effectively satisfying the user requirements and significantly improving the user experience.

Abstract: A method, an electronic device, and a computer program product for generating a three-dimensional scene are provided in embodiments of the present disclosure. The method may include obtaining source image features from a plurality of two-dimensional source images associated with the three-dimensional scene to be generated. The method may further include obtaining editing features from an editing instruction input by a user for the three-dimensional scene, each of the editing features respectively forming a feature pair with each of the source image features. Furthermore, the method may include updating the source image features by maximizing a correlation coefficient of each of the feature pairs, and generating the three-dimensional scene based at least on the updated source image features. Embodiments of the present disclosure can realize arbitrary editing of a three-dimensional scene, thus enhancing the experience of human-computer interaction.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for processing data. The method includes determining encoded data for a target object by performing hybrid encoding on multiple types of data for the target object, the multiple types of data including image data of the target object. The method further includes determining, by transforming the encoded data, an invariant portion for a shape of the target object and a variable portion for an expression of the target object. The method improves data processing efficiency, saves time and computing resources, and reduces the amount of data transmission.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for rendering a target scene. The method includes acquiring from a server rendering data including a point cloud and a color of the target scene. The method further includes determining a pose used for rendering the target scene at user equipment. The method further includes rendering the target scene on the basis of the rendering data and the pose, so as to show the target scene in the pose. In embodiments of the present disclosure, a rendering effect on the target scene can be improved, and the rendering speed is higher.

Abstract: Embodiments disclosed herein relate to a method, an electronic device, and a computer program product for data anonymization. The method includes: performing classification on data by a classifier to obtain data types of the data. The method further includes: performing anonymization on the data by a first anonymization model to obtain first anonymized data. The method further includes: determining, based on the data types, using an anonymizer whether re-anonymization needs to be performed on the first anonymized data. The method further includes: performing, based on a determination that the re-anonymization needs to be performed, the re-anonymization on the first anonymized data by a second anonymization model to obtain second anonymized data. Accordingly, anonymization processing may be performed on data using different anonymization models for different types of data to obtain the final anonymized data and to ensure that no data leakage occurs.

Abstract: Illustrative embodiments of the present disclosure include a method, an electronic device, and a computer program product for image processing. The method includes extracting a first hidden vector in a first image and acquiring first grid data associated with the first image, wherein the first grid data corresponds to pixel positions of the first image. The method further includes encoding the first hidden vector to acquire first encoded data, generating first inference data based on the first grid data and the first encoded data, decoding the first inference data to generate first decoded data, and generating a second image based on the first decoded data and the first image, wherein the second image has a higher resolution than the first image. The method can continuously enhance image quality by exploring potential correlations between pixels, and can provide powerful and arbitrary super resolution processing capabilities for up sampled images.

Abstract: A method includes: determining a first sample subset in an initial sample set by an initial model based on the initial sample set, wherein the initial sample set comprises a plurality of question-answer pairs, each of the plurality of question-answer pairs comprising a question and an answer; generating a first model by training the initial model with the first sample subset; determining a second sample subset in the first sample subset by the first model based on the first sample subset; generating a second model by training the first model with the second sample subset; determining, in response to at least one of the second sample subset and the second model satisfying a corresponding predetermined condition, a third sample subset of the initial sample set by the second model based on the initial sample set; and generating a third model by training the second model with the third sample subset.

Abstract: The present disclosure relates to a method, a device, and a computer program product for model comparison. The method includes generating a detection image based on an original image. The method further includes obtaining a first classification result by sending the detection image to a target model, and obtaining a second classification result by sending the detection image to a to-be-detected model. In addition, the method further includes comparing the first classification result with the second classification result, and determining, in response to the first classification result being the same as the second classification result, that the target model is the same as the to-be-detected model. The method of the present disclosure can verify whether the to-be-detected model plagiarizes the target model without knowing any internal structure, parameters, weights, and other information of the to-be-detected model.

Abstract: Embodiments of the present disclosure provide a method for image processing. The method comprises extracting a target region in an image, and determining hidden vectors of the image based on the target region. The method further comprises generating a first classification result for the image based on the hidden vectors, obtaining a second classification result for the image from an image classification model, and determining trustworthiness of the image based on the first classification result and the second classification result. By using the method, a more flexible framework suitable for different applications can be provided. The framework not only can support machine learning models based on different algorithms, but also can specify data distribution more accurately. In addition, a method for independently detecting adversarial attacks is provided, thus improving the security of image detection.

Abstract: A method in an illustrative embodiment includes: acquiring an image set, where the image set includes a first plurality of images that can be classified into at least two categories; determining a corner case image set in the image set, where the corner case image set includes a second plurality of images that tend to be incorrectly classified; training an image generator with at least some images in the second plurality of images and first guidance associated with the at least some images; and generating an additional image by the trained image generator with a first image in the second plurality of images and additional guidance, where the additional guidance is different from the first guidance. By means of the technical solutions of the present disclosure, an image generation efficiency can be improved, and the quality of generated images can be enhanced, thereby improving user experience.

Abstract: The present disclosure relates to a method, a device, and a computer program product for generating video. The method includes extracting a first text of a first language in an input video and generating a second text of a second language corresponding to the first text of the first language. The method further includes generating a gist frame of the input video based on the input video and the second text, where the gist frame indicates information associated with color configuration and object layout of the input video. In addition, the method further includes generating, based on the gist frame and the second text, an output video broadcasting the second text by means of the second language. The method of the present disclosure enhances the similarity of the output video and the input video, and expressions and actions of a narrator can be generated at a correct time point.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for video retrieval. The method includes determining a retrieval level corresponding to a retrieval word in response to receiving a retrieval request including the retrieval word from a client. The method further includes determining a video database corresponding to the retrieval level among a plurality of video databases, where the plurality of video databases store image frames for different frame rates of the same video. The method further includes retrieving an image frame associated with the retrieval word from the determined video database and sending the retrieved image frame to the client. According to the solution, multi-level video retrieval can be realized, allowing a user to retrieve a desired video in different scenes or different devices at different retrieval speeds, so as to provide the user with a more flexible video retrieval mode.

Abstract: A method in an illustrative embodiment includes acquiring a first sequence of a set of image frames of a video arranged in positive order. The method further includes generating, based on the first sequence of a positive-order arrangement, a second sequence of the set of image frames arranged in reverse order. The method further includes determining a first cross correlation sequence of the image frames in the first sequence relative to the image frames in the second sequence; determining a second cross correlation sequence of the image frames in the second sequence relative to the image frames in the first sequence; determining, based on the first cross correlation sequence and the second cross correlation sequence, a global feature distribution for the set of image frames; and finally, determining, based on the global feature distribution, at least one target frame in the set of image frames.

Abstract: Embodiments of the present disclosure relate to a method, a device, and a computer program product for image processing. The method includes: obtaining a target object segmentation map for a target image and a reference dress-up segmentation map for a reference image, wherein the target image presents a target object, and the reference image presents reference dress-up; extracting a target image feature of the target image and a reference image feature of the reference image by means of image encoding of the target image and the reference image; and transferring the reference dress-up in the reference image to the target object based on the target object segmentation map, the reference dress-up segmentation map, the target image feature, and the reference image feature.

Abstract: Embodiments of the present disclosure provide a method, an electronic device, and a computer program product for generating logs. The method comprises: generating, by a generator, a pseudo-log based on a generating condition and random noise; determining, by a discriminator, event types of log messages in a real log and the pseudo-log; determining, by the discriminator, a feature matrix of log sequences in the real log and the pseudo-log based on the event types; determining, by the discriminator, authenticity of the real log and the pseudo-log based on the feature matrix; and training the generator and the discriminator based on the authenticity. The method according to embodiments of the present disclosure may improve the accuracy of a training model for generating pseudo-logs that are sufficiently real.