Abstract: A human body identification method, an electronic device and a storage medium, related to the technical field of artificial intelligence such as computer vision and deep learning, are provided. The method includes: inputting an image to be identified into a human body detection model, to obtain a plurality of preselected detection boxes; identifying a plurality of key points from each of the preselected detection boxes respectively according to a human body key point detection model, and obtaining a key point score of each of the key points; determining a target detection box from each of the preselected detection boxes, according to a number of the key points whose key point scores meet a key point threshold; and inputting the target detection box into a human body key point classification model, to obtain a human body identification result for the image to be identified.

Abstract: The present disclosure provides a method, an apparatus, a computer device and a computer-readable storage medium for positioning, and relates to the field of autonomous driving. The method obtains point cloud data collected by a LiDAR on a device at a current time; determines, based on the point cloud data and a global map built in a global coordinate system, global positioning information of the device in the global coordinate system at the current time; and determine, based on the point cloud data and a local map built in a local coordinate system, local positioning information of the device in the local coordinate system at the current time. A positioning result of the device at the current time is determined based on at least the global positioning information and the local positioning information. Techniques of the present disclosure can provide an effective and stable positioning service.

Abstract: A method, apparatus, device and storage medium for recognizing a bill image may include: performing text detection on a bill image, and determining an attribute information set and a relationship information set of each text box of at least two text boxes in the bill image; determining a type of the text box and an associated text box that has a structural relationship with the text box based on the attribute information set and the relationship information set of the text box; and extracting structured bill data of the bill image, based on the type of the text box and the associated text box that has the structural relationship with the text box.

Abstract: A method for training generative network, a method for generating near-infrared image and device. The method includes: obtaining a training sample set, in which the set includes near-infrared image samples and visible-light image samples; obtaining an adversarial network to be trained, in which the generative network of the adversarial network is configured to generate each near-infrared image according to an input visible-light image, the discrimination network of the adversarial network is configured to determine whether an input image is real or generated; constructing a first objective function according to a first distance between each generated near-infrared image and the corresponding near-infrared image sample in an image space and a second distance between each generated near-infrared image and the corresponding near-infrared image sample in a feature space; performing an adversarial training on the adversarial network with the set based on optimizing a value of the first objective function.

Abstract: A method, apparatus, and electronic device for processing a video, a medium and a product are presented. An implementation of the method includes: acquiring a target video; selecting, from at least one preset model, a preset model as a target model; determining output data of the target model based on the target video and the target model; reselecting, in response to determining that the output data does not meet a condition corresponding to the target model, another preset model as the target model from the at least one preset model until the output data of the target model meets the condition corresponding to the target model; and determining, based on the output data, a dynamic cover from the target video.

Abstract: The present disclosure provides a method for visual question answering, which relates to fields of computer vision and natural language processing. The method includes: acquiring an input image and an input question; detecting visual information and position information of each of at least one text region in the input image; determining semantic information and attribute information of each of the at least one text region based on the visual information and the position information; determining a global feature of the input image based on the visual information, the position information, the semantic information, and the attribute information; determining a question feature based on the input question; and generating a predicted answer for the input image and the input question based on the global feature and the question feature. The present disclosure further provides a device for visual question answering, a computer device and a medium.

Abstract: The present disclosure discloses a method and apparatus for acquiring point of interest (POI) state information, and relates to a big data technology in the technical field of artificial intelligence.

Abstract: A method, electronic device and storage medium for generating information are disclosed. The method includes: acquiring a plurality of tag entity words from a target video, the tag entity words including a person entity word, a work entity word, a video category entity word, and a video core entity word, the video core entity word including an entity word for characterizing a content related to the target video; linking, for a tag entity word among the plurality of tag entity words, the tag entity word to a node of a preset knowledge graph; determining semantic information of the target video based on a linking result of each of the tag entity words; and structuring the semantic information of the target video based on a relationship between the node and an edge of the knowledge graph, to obtain structured semantic information of the target video.

Abstract: The present application discloses a method and apparatus for training a retrieval model, device and computer storage medium that relate to intelligent search and natural language processing technologies. An implementation includes: acquiring initial training data; performing a training operation using the initial training data to obtain an initial retrieval model; selecting texts with the correlation degrees with a query in the training data meeting a preset first requirement from candidate texts using the initial retrieval model; performing a training operation using the updated training data to obtain a first retrieval model; and selecting texts with the correlation degrees with the query in the training data meeting a preset second requirement from the candidate texts using the first retrieval model; and/or selecting texts with the correlation degrees with the query meeting a preset third requirement; and performing a training operation using the expanded training data to obtain a second retrieval model.

Abstract: Provided is a method for detecting live streaming jitter, a device, and a medium. An implementation is: calculating, for a live stream transmitted by an edge content delivery network (CDN) node in a CDN, quality information of the live stream based on a transmission frame rate and a viewer count of the live stream; calculating quality information of the edge CDN node based on the quality information of the live stream; and determining, based on the quality information of the edge CDN node, whether jitter occurs at the edge CDN node.

Abstract: The present disclosure discloses a method for processing a slider for a virtual character, an electronic device, and a storage medium, relating to a field of virtual reality, in particular to fields of artificial intelligence, Internet of Things, voice technology, cloud computing, etc. An implementation includes: acquiring a shape model associated with a target semantic tag; acquiring a skeleton and skinning information of a reference virtual character; fitting the shape model based on the skeleton and skinning information to obtain a skeleton linkage coefficient; and generating a slider associated with the target semantic tag based on the skeleton linkage coefficient, wherein the slider is used to drive the reference virtual character to obtain a target virtual character complying with a target semantic feature contained in the target semantic tag.

Abstract: The present disclosure discloses a method for training a reading comprehension model, and relates to a field of natural language processing and deep learning technologies. The detailed implementing solution includes: respectively inputting a first training sample of the reference field into a reference reading comprehension model of a reference field and a target reading comprehension model of a target field, to obtain first output data output by the reference reading comprehension model and second output data output by the target reading comprehension model; and performing a first training process on the target reading comprehension model based on a difference between the first output data and the second output data.

Abstract: The present application provides a model training method and apparatus, and a prediction method and apparatus, and it relates to fields of artificial intelligence, deep learning, image processing, and autonomous driving. The model training method includes: inputting a first sample image of sample images into a depth information prediction model, and acquiring depth information of the first sample image; acquiring inter-image posture information based on a second sample image of the sample images and the first sample image; acquiring a projection image corresponding to the first sample image, at least according to the inter-image posture information and the depth information; and acquiring a loss function by determining a function for calculating a similarity between the second sample image and the projection image, and training the depth information prediction model using the loss function.

Abstract: The present application discloses a positioning method and an apparatus, which relate to the technical field of intelligent driving. A specific implementation solution is: determining a first positioning result using point cloud data collected by lidar in combination with a laser point cloud reflection value map; constructing a constraint condition using the first positioning result, where the constraint condition is used to accelerate a convergence speed of solving a receiver position using observation data; performing GNSS-PPP positioning using the constraint condition in combination with observation data of a GNSS receiver to obtain a second positioning result. Using this solution, lidar positioning technology is combined with GNSS-PPP positioning technology to realize a purpose of not relying on a GNSS base station.

Abstract: A computer-implemented method for speech recognition is disclosed. The method includes extracting a feature word associated with location information from a speech to be recognized, and calculating a similarity between the feature word and respective ones of a plurality of candidate words in a corpus. The corpus includes a first sub-corpus associated with at least one user, and the plurality of candidate words include, in the first sub-corpus, a first standard candidate word and at least one first erroneous candidate word. The at least one first erroneous candidate word has a preset correspondence with the first standard candidate word. The method further includes in response to the similarity between the feature word and one or more of the at least one first erroneous candidate word satisfying a predetermined condition, outputting the first standard candidate word as a recognition result based on the preset correspondence.

Abstract: A method for identifying a user includes: controlling an electronic device to connect to a first communication network; obtaining target behavior data of a user to be identified from a data pool corresponding to the first communication network, in which, the data pool stores at least one type of candidate behavior data of a candidate user, the candidate behavior data is obtained from a data source corresponding to a second communication network, and a security level of the first communication network is higher than a security level of the second communication network; and obtaining a category of the user to be identified by analyzing the target behavior data based on the first communication network.

Abstract: Provided are a video generation method and apparatus, a device and a storage medium, relating to the field of artificial intelligence and, in particular, to the fields of computer vision and deep learning. The method includes changing a character emotion of an original character image according to a character emotion feature of a to-be-generated video to obtain a target character image; and driving the target character image by use of a character driving network and based on a speech segment to obtain the to-be-generated video.

Abstract: A method and apparatus for optimizing a recommendation system, a device and a computer storage medium are described, which relates to the technical field of deep learning and intelligent search in artificial intelligence. A specific implementation solution is: taking the recommendation system as an agent, a user as an environment, each recommended content of the recommendation system as an action of the agent, and a long-term behavioral revenue of the user as a reward of the environment; and optimizing to-be-optimized parameters in the recommendation system by reinforcement learning to maximize the reward of the environment. The present disclosure can effectively optimize long-term behavioral revenues of users.

Abstract: The present disclosure discloses a method and apparatus for recognizing a text. The method comprises: acquiring images of a text area of an input image, the acquired images including a text centerline graph, a text direction offset graph, a text boundary offset graph, and a text character classification graph; extracting coordinates of feature points of a character center from the text centerline graph; sorting the extracted coordinates of the feature points based on the text direction offset graph to obtain a coordinate sequence of the feature points; determining a polygonal bounding box of the text area based on the coordinate sequence of the feature points of the character center and the text boundary offset graph; and determining a classification result of the feature points of the character center, based on the coordinate sequence of the feature points of the character center and the text character classification graph.

Abstract: Methods and apparatuses for generating a model and generating a 3D animation, devices, and storage mediums are provided. The method for generating a model may include: acquiring a preset sample set; acquiring pre-established generative adversarial nets, the generative adversarial nets including a generator and a discriminator; and performing training steps as follows: selecting a sample from the sample set; extracting a sample audio feature from the sample audio of the sample; inputting the sample audio feature into the generator to obtain a pseudo 3D mesh vertex sequence of the sample; inputting the pseudo 3D mesh vertex sequence and the real 3D mesh vertex sequence of the sample into the discriminator to discriminate authenticity of 3D mesh vertices; and in response to determining that the generative adversarial nets meet a training completion condition, obtaining a trained generator as a model for generating a 3D animation.