Abstract: Provided are a method and apparatus for processing map information, a device, and a storage medium which relate to the field of computer technology and, in particular, to the fields of intelligent transportation and computer vision technology. The specific implementation includes that an object in an image for query is recognized to obtain an object recognition result and that a target point of interest matching the image for query is selected from at least one candidate point of interest of an electronic map according to the object recognition result.

Abstract: The present disclosure provides a method and system for processing an obstacle detection result of an ultrasonic sensor array. The method comprises: obtaining obstacle coordinates collected by ultrasonic sensors in an ultrasonic sensor array; inputting the obstacle coordinates collected by ultrasonic sensors in the ultrasonic sensor array into a pre-trained neural network model, to obtain true or false labels for the obstacle coordinates collected by the ultrasonic sensors output by the pre-trained neural network model; process the obstacle coordinates collected by the ultrasonic sensors in the ultrasonic sensor array according to the true or false labels. The present disclosure improves the accuracy in the obstacle detection of the ultrasonic sensor array, avoids detection error and omission and improves the driving safety.

Abstract: The present disclosure provides an ultrasonic radar array, an obstacle detection method and system. The method includes: obtaining obstacle information collected by ultrasonic radars in an ultrasonic radar array in an obstacle scenario; judging false detection and missed detection for the obstacle information collected by ultrasonic radars according a preset rule; processing the obstacle information collected by the ultrasonic radars according to the judgement result; determining a position of the obstacle according to the processed obstacle information collected by the ultrasonic radars.

Abstract: The present disclosure provides an event extraction method, an event extraction device and an electronic device, and it relates to the field of computer data processing, in particular to the field of knowledge graph. The event extraction method includes: acquiring text information; determining a plurality of pieces of question information ranked in a sequential order in accordance with the text information; and inputting vector information for each piece of question information into an extraction model in accordance with the sequential order to acquire extraction information for each piece of question information.

Abstract: The present application provides a model training, image processing method, device, storage medium, and program product relating to deep learning technology, which are able to screen auxiliary image data with image data for learning a target task, and further fuse the target image data and the auxiliary image data, so as to train a built and to-be-trained model with the fusion-processed fused image data. This implementation can increase the amount of data for training the model, and the data for training the model is determined is based on the target image data, which is suitable for learning the target task. Therefore, the solution provided by the present application can train an accurate target model even if the amount of target image data is not sufficient.

Abstract: The present application provides an image processing method, an image processing apparatus, an electronic device and a computer-readable storage medium, and relates to the field of image processing technologies. An implementation includes: acquiring an image to be processed; converting the image to be processed into a three-channel YUV image; performing a convolution operation on a Y-channel image, a U-channel image and a V-channel image in the three-channel YUV image to generate an R-channel image, a G-channel image and a B-channel image, respectively, and acquiring a three-channel RGB image; and pre-processing the three-channel RGB image. According to the present application, the image pre-processing speed can be improved.

Abstract: A multi-modal pre-training model acquisition method, an electronic device and a storage medium, which relate to the fields of deep learning and natural language processing, are disclosed. The method may include: determining, for each image-text pair as training data, to-be-processed fine-grained semantic word in the text; masking the to-be-processed fine-grained semantic words; and training the multi-modal pre-training model using the training data with the fine-grained semantic words masked.

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: 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: 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: 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: 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 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: 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.