Abstract: A method for text generation, relates to a field of natural language processing, including: obtaining corpus data; labeling the corpus data to obtain a first constraint element; obtaining a first generation target; and generating a first text matching the first generation target by inputting the corpus data and the first constraint element into a generation model.

Abstract: This disclosure provides a video generation method, a video generation apparatus, an electronic device, a storage medium and a program product, and relates to the field of artificial intelligence technology, and in particular to the field of computer vision technology and deep learning technology. A specific implementation includes: obtaining document content information of a document; extracting, from the document content information, populating information for multiple scenes in a preset video template; populating the populating information for the multiple scenes into corresponding scenes in the preset video template, respectively, to obtain image information of the multiple scenes; generating audio information of the multiple scenes according to the populating information for the multiple scenes; generating a video of the document based on the image information and audio information of the multiple scenes.

Abstract: The present disclosure provides a method and apparatus of editing a multimedia file, an electronic device, and a storage medium. The method includes: acquiring a multimedia file and an operation information contained in each editing track of a plurality of editing tracks, wherein the operation information is acquired to edit the multimedia file; detecting a multimedia file frame contained in the each editing track at a current time in a case of editing the multimedia file in a chronological order; editing the multimedia file frame contained in the each editing track at the current time by using the operation information associated with the multimedia file frame, so as to obtain an edited file frame contained in the each editing track at the current time; and performing an encoding synthesis on the edited file frame contained in the each editing track at the current time.

Abstract: Provided are a method and apparatus for evaluating image relative definition, a device and a medium, relating to technologies such as computer vision, deep learning and intelligent medical. A specific implementation solution is: extracting a multi-scale feature of each image in an image set, where the multi-scale feature is used for representing definition features of objects having different sizes in an image; and scoring relative definition of each image in the image set according to the multi-scale feature by using a relative definition scoring model pre-trained, where the purpose for training the relative definition scoring model is to learn a feature related to image definition in the multi-scale feature.

Abstract: A method for operating a quantum computer is provided. The method includes: obtaining a quantum gate parameter of each quantum gate in a quantum circuit to be simulated; and generating a sub-measurement mode equivalent to the quantum gate based on the quantum gate parameter. The method further includes combining the sub-measurement mode equivalent to each quantum gate to obtain an overall measurement mode equivalent to the quantum circuit to be simulated as a whole; and sorting an operation order of the operation commands of each sub-measurement mode in the overall measurement mode, thereby obtaining the sorted overall measurement mode as a simulation result. This solution transforms the quantum circuit used for quantum computation into an equivalent measurement mode according to a one-way quantum computer computation model. Direct simulation computation on the quantum circuit can be avoided, and accordingly a simulation operation amount of a classical computer is greatly reduced.

Abstract: The present disclosure provides a method of training a depth estimation network, which relates to fields of computer vision, deep learning, and image processing technology. The method includes: performing a depth estimation on an original image by using a depth estimation network, so as to obtain a depth image for the original image; removing a moving object from the original image so as to obtain a preprocessed image for the original image; estimating a pose based on the original image and modifying the pose based on the preprocessed image; and adjusting parameters of the depth estimation network according to the original image, the depth image and the pose modified. The present disclosure further provides an apparatus of training a depth estimation network, a method and apparatus of estimating a depth of an image, an electronic device, and a storage medium.

Abstract: A method, an apparatus, device and a storage medium for generating a target re-recognition model are provided. The method may include: acquiring a set of labeled samples, a set of unlabeled samples and an initialization model obtained through supervised training; performing feature extraction on each sample in the set of the unlabeled samples by using the initialization model; clustering features extracted from the set of the unlabeled samples by using a clustering algorithm; assigning, for each sample in the set of the unlabeled samples, a pseudo label to the sample according to a cluster corresponding to the sample in a feature space; and mixing a set of samples with a pseudo label and the set of the labeled samples as a set of training samples, and performing supervised training on the initialization model to obtain a target re-recognition model.

Abstract: The present application discloses a method and apparatus for training a semantic representation model, a device and a computer storage medium, which relates to the field of natural language processing technologies in artificial intelligence.

Abstract: Embodiments of the present disclosure disclose a method and apparatus for determining a target anchor, a device and a storage medium. The method may include: extracting a plurality of feature maps of an original image using a feature extraction network; inputting the plurality of feature maps into a feature pyramid network to perform feature fusion, to obtain a plurality of fused feature maps; and using a region proposal network to implement operations as follows: determining an initial anchor of a network head using the fused feature map, based on a size of each fused feature map, and determining an offset parameter of the initial anchor, based on a ratio of the size of the fused feature map to the original image, and generating a plurality of candidate anchors in different directions, based on the offset parameter of the initial anchor.

Abstract: A cloud computing resource extension method, a Smart Local Compute Cluster (SLCC), and a storage medium are suggested, which relate to the field of artificial intelligence. The method of cloud computing resource extension includes: acquiring registration information of an extended Availability Zone (AZ), the extended AZ being located in a user's local computer room; sending the registration information to a public cloud, the public cloud being configured to register, based on the registration information, the extended AZ with an AZ set managed and controlled by the public cloud and generate deployment information of a target cloud service pre-designated by the user; receiving the deployment information sent by the public cloud; and deploying the target cloud service in the extended AZ based on the deployment information.

Abstract: The present disclosure provides a method and apparatus for coupling a superconducting qubit. The method includes: determining a target coupling strength between a target read cavity and a qubit, a first target frequency of the qubit, and a second target frequency of the target read cavity; initializing a read coupling port configuration layout based on a configuration of the qubit, a relative position of the qubit to the target read cavity; calculating a to-be-measured coupling strength between the qubit and the read coupling port, based on the read coupling port configuration layout, the first target frequency, and the second target frequency; and in response to detecting that the to-be-measured coupling strength and the target coupling strength satisfy a preset condition, generating, based on the second target frequency and the read coupling port configuration layout, a complete layout comprising the qubit and the target read cavity.

Abstract: The present disclosure discloses a vehicle control method and apparatus, a device and a computer storage medium, and relates to the technical fields of autonomous driving and intelligent transportation. A specific implementation solution involves: determining vehicles in a preset geo-fencing region; determining a vehicle weight of each said vehicles according to a vehicle type and a waiting duration of each said vehicles; estimating, according to the vehicle weights of the vehicles in each of lanes in the geo-fencing region and positions of the vehicles in each said lanes, a duration to be waited in each said lanes; and generating a control instruction for each said vehicles according to the respective durations to be waited in each said lanes and the respective positions of the vehicles in each said lanes, the control instruction including a state instruction and/or a target speed instruction.

Abstract: The disclosure provides a method and a device for processing information, an electronic device, and a storage medium, belonging to a field of artificial intelligence including computer vision, deep learning, and natural language processing. In the method, the computing device recognizes multiple text items in the image. The computing device classifies multiple text items into a first set of name text items and a second set of content text items based on semantics of the text items. The computing device performs a matching operation between the first set and the second set based on a layout of the text items in the image, and determines matched name-content text items. The matched name-content text items include a name text item in the first set and a content text item matching the name text item and in the second set. The computing device outputs the matched name-content text items.

Abstract: Embodiments of the present disclosure provide methods and apparatuses for blockchain-based multi-party computation, a device and a medium, relate to blockchain technology in the field of computer technology. An embodiment of the method can include: encrypting business data, to obtain a ciphertext of the business data; hashing the ciphertext of the business data, to obtain a hash result of the business data; sending the hash result of the business data to a blockchain node, so that the blockchain node writes the hash result of the business data into a blockchain; and sending the ciphertext of the business data to a target trusted computing module in a target server, for instructing the target trusted computing module to perform multi-party computation based on the ciphertext of the business data and the hash result of the business data in the blockchain.

Abstract: The present disclosure provides a method for selecting an annotated sample. The method includes: determining a first attribute and a second attribute of a sample characteristic; in which the first attribute is a characteristic attribute of the sample characteristic in a source field sample set, and the second attribute is a characteristic attribute of the sample characteristic in a target field sample set; and determining a target annotated sample from a plurality of candidate annotated samples of the source field sample set according to the first attribute and the second attribute; in which the target annotated sample is configured to train a classification model, the classification model includes a model for determining an emotion polarity by analyzing an input sample to be classified.

Abstract: The disclosure provides a role labeling method. The method includes: obtaining a dialogue sentence to be labeled and context information corresponding to the dialogue sentence, and splicing the context information and the dialogue sentence to obtain a spliced text; extracting location information of a role name of the dialogue sentence in the spliced text from the spliced text; determining a first candidate role name of the dialogue sentence based on the location information; determining a second candidate role name of the dialogue sentence from role names in the spliced text; and determining a target role name of the dialogue sentence based on the first candidate role name and the second candidate role name, and performing role labeling on the dialogue sentence based on the target role name.

Abstract: Exemplary embodiments of the present disclosure provide a method and apparatus for managing a knowledge base, a device and a computer readable storage medium. The method for managing a knowledge base includes: forking, in response to receiving a request for modification of a rule for a first namespace, the rule to the first namespace, the rule being used to constrain structured data in a knowledge base; modifying, based on the request for modification of the rule, the rule in the first namespace; and adding the modified rule to a rule base associated with the knowledge base.

Abstract: The present disclosure provides a method and apparatus for processing a streaming media service, an electronic device, and a storage medium, and relates to the technical field of computers, particularly to technical fields such as industrial vision, deep learning, streaming media, and information flow. A specific implementation solution involves: acquiring registration information of an input source, the registration information including process information of a streaming media service process of the input source and streaming media address information of the input source; enabling the streaming media service process according to the process information; and controlling, by using the streaming media address information, the streaming media service process to process streaming media data of the input source.

Abstract: This application discloses a keyword generating method, an apparatus, a device and a storage medium, which relate to the field of natural language processing in the field of artificial intelligence. A specific implementation scheme includes: inputting a target text into a text processing model, obtaining a word sequence corresponding to the target text, and generating a semantic representation sequence corresponding to the word sequence; making prediction about each semantic representation vector in the semantic representation sequence respectively to obtain a prediction result; and if the prediction result indicates that a word corresponding to the semantic representation vector is capable of triggering a generation of a keyword, outputting the keyword based on the semantic representation vector and the prediction result. This method improves the accuracy of generating keywords.

Abstract: The present disclosure provides a method for training a linguistic model, related to fields of speech, natural language processing, deep learning technologies. A method includes: obtaining grammars corresponding to a plurality of sample texts and a slot value of a slot in each grammar by using semantic analysis; generating a grammar graph corresponding to each grammar based on the corresponding grammar and the slot value of the slot in the corresponding grammar; obtaining a weight of each grammar, a weight of each slot, and a weight of each slot value in each grammar graph based on the sample texts; determining at least one grammar frequency of each order based on the weight of each grammar, the weight of each slot, and the weight of each slot value in each grammar graph; and training the linguistic model based on the at least one grammar frequency of each order.