Abstract: A method for model aggregation in federated learning (FL), a server, a device, and a storage medium are suggested, which relate to the field of artificial intelligence (AI) technologies such as machine learning. A specific implementation solution involves: acquiring a data not identically and independently distributed (Non-IID) degree value of each of a plurality of edge devices participating in FL; acquiring local models uploaded by the edge devices; and performing aggregation based on the data Non-IID degree values of the edge devices and the local models uploaded by the edge devices to obtain a global model.

Abstract: A road congestion detection method realized by a computer includes obtaining K images taken by L cameras corresponding to a first road at a same target time point, the first road including M road sections, each road section corresponding to at least one camera, M, L, K each being a positive integer, L being greater than or equal to M, performing target detection on each image to obtain area ratio information, the area ratio information representing a ratio of an area of vehicles on a lane of a target road section to an area of the lane of the target road section, the target road section being a road section corresponding to the image in the M road sections, and determining a first congestion detection result of the first road at the target time point in accordance with K pieces of area ratio information corresponding to the M road sections.

Abstract: The present disclosure provides a method and apparatus of estimating a road condition, and a method and apparatus of establishing a road condition estimation model, which relates to a field of big data and intelligent traffic. The method includes: acquiring, for a first preset duration before a first moment, a sequence of user tracks for a road and a sequence of road images for the road; extracting a track-related feature of the road from the sequence of the user tracks, and extracting an image-related feature of the road from the sequence of the road images; and inputting the track-related feature of the road and the image-related feature of the road into a pre-trained road condition estimation model, so as to determine, for a second preset duration after the first moment, a road condition information of the road by using an estimated result of the road condition estimation model.

Abstract: The present disclosure provides an object pose obtaining method, and an electronic device, relates to technology fields of image processing, computer vision, and deep learning. A detailed implementation is: extracting an image block of an object from an image, and generating a local coordinate system corresponding to the image block; obtaining 2D projection key points in an image coordinate system corresponding to a plurality of 3D key points on a 3D model of the object; converting the 2D projection key points into the local coordinate system to generate corresponding 2D prediction key points; obtaining direction vectors between each pixel point in the image block and each 2D prediction key point, and obtaining a 2D target key point corresponding to each 2D predication key point based on the direction vectors; and determining a pose of the object according to the 3D key points and the 2D target key points.

Abstract: A vehicle control method, a vehicle control apparatus, an electronic device and a self-driving vehicle all relates to the field of self-driving and intelligent transportation technologies. The method includes: when a vehicle is moving, in a case that an occluding object is detected, determining a hard brake speed limit point and a potential collision point according to a planned path of the vehicle and position information of the occluding object; calculating a speed limit value of the hard brake speed limit point based on a distance between the hard brake speed limit point and the potential collision point; in a case that a planned speed of the vehicle at the hard brake speed limit point is less than or equal to the speed limit value, controlling the vehicle to move at the planned speed.

Abstract: A method for detecting a face synthetic image, an electronic device and a storage medium are provided. The technical solution includes inputting a face image to be detected into a pre-trained convolution neural network to obtain a raw image feature of the face image; inputting the raw image feature into a first full connected layer and a second full connected layer respectively to obtain a first feature vector corresponding to a face key point of the face image and a second feature vector corresponding to the face image; merging the first feature vector and the second feature vector to obtain a merged feature vector; inputting the merged feature vector to a third full connected layer to obtain a detection result of the face image.

Abstract: An event argument extraction (EAE) method, an EAE apparatus and an electronic device, relates to the technical field of knowledge graphs. A specific implementation scheme includes acquiring a to-be-extracted event content; and performing argument extraction on the to-be-extracted event content based on a trained EAE model, to obtain a target argument of the to-be-extracted event content; where the trained EAE model is obtained by training a pre-trained model with event news annotation data and a weight of each argument annotated in the event news annotation data.

Abstract: Embodiments of the present disclosure provide a container image processing method and apparatus, and a storage medium. The method includes: obtaining required file information for generating a container image, and constructing a complete image according to the required file information; determining a specified directory to be deleted in the complete image, and compressing the complete image based on the specified directory to be deleted to obtain a compressed image; obtaining directory content of the specified directory to be deleted, and generating content to be mounted based on the directory content of the specified directory to be deleted; and storing the content to be mounted to a block device file system, and mounting the compressed image to the block device file system.

Abstract: A method and apparatus for processing an image, a device and a storage medium are provided. An implementation of the method includes: acquiring a template image, the template image including at least one region of interest; determining a first feature map corresponding to each region of interest in the template image; acquiring a target image; determining a second feature map of the target image; and determining at least one region of interest in the target image according to the first feature map and the second feature map.

Abstract: There is provided an image-based information extraction model, method, and apparatus, a device, and a storage medium, which relates to the field of artificial intelligence (AI) technologies, specifically to fields of deep learning, image processing, computer vision technologies, and is applicable to optical character recognition (OCR) and other scenarios. A specific implementation solution involves: acquiring a to-be-extracted first image and a category of to-be-extracted information; and inputting the first image and the category into a pre-trained information extraction model to perform information extraction on the first image to obtain text information corresponding to the category.

Abstract: A method and an apparatus for calibrating an external parameter of a camera are provided.

Abstract: Provided are a method for training a font generation model, a method for establishing a font library, and a device. The method for training a font generation model includes the following steps. A source-domain sample character is input into the font generation model to obtain a first target-domain generated character. The first target-domain generated character is input into a font recognition model to obtain the target adversarial loss of the font generation model. The model parameter of the font generation model is updated according to the target adversarial loss.

Abstract: A meme generation method, an electronic device, and a storage medium are provided. The method includes: determining a plurality of second expression images corresponding to a target face image based on a plurality of first expression images contained in a first meme; generating a second meme corresponding to the target face image based on the plurality of second expression images corresponding to the target face image; wherein, determining an affine transformation parameter between the target face image and an i-th first expression image in the plurality of first expression images according to a corresponding relation between a face key point in the target face image and a face key point in the i-th first expression image; and transforming the target face image based on the affine transformation parameter to obtain an i-th second expression image corresponding to the target face image.

Abstract: There is provided cross-modal feature extraction, retrieval, and model training methods and apparatuses, and a medium, which relates to the field of artificial intelligence (AI) technologies, and specifically to fields of deep learning, image processing, and computer vision technologies. A specific implementation solution involves: acquiring to-be-processed data, the to-be-processed data corresponding to at least two types of first modalities; determining first data of a second modality in the to-be-processed data, the second modality being any of the types of the first modalities; performing semantic entity extraction on the first data to obtain semantic entities; and acquiring semantic coding features of the first data based on the first data and the semantic entities and by using a pre-trained cross-modal feature extraction model.

Abstract: The present application discloses a method and an apparatus for processing map data, which relate to autonomous driving technologies in the field of data processing. The specific implementation is that: a controlling unit inputs initial positioning data collected by a data collecting unit to a data fusing unit to obtain fused positioning data, where the initial positioning data and the fused positioning data are data in a first coordinate system; the controlling unit obtains target positioning data according to the fused positioning data, where the target positioning data is data in a second coordinate system, and the second coordinate system is a coordinate system obtained by offsetting the first coordinate system; and the controlling unit performs a positioning operation on the target positioning data through at least one positioning unit, to determine a position of a vehicle corresponding to an autonomous driving system.

Abstract: A vehicle information detection method, a method for training a detection model, an electronic device and a storage medium are provided, and relates to the technical field of artificial intelligence, in particular to the technical field of computer vision and deep learning. The method includes: performing a first target detection operation based on an image of a target vehicle, to obtain a first detection result for target information of the target vehicle; performing an error detection operation based on the first detection result, to obtain error information; and performing a second target detection operation based on the first detection result and the error information, to obtain a second detection result for the target information.

Abstract: Provided are a streaming media processing method based on inference service, an electronic device, and a storage medium, which relates to the field of artificial intelligence, and in particular, to the field of inference service of artificial intelligence models. The method includes: detecting, in a process of processing a k-th channel of streaming media through an i-th inference service pod, the i-th inference service pod, to obtain a detection result of the i-th inference service pod, i and k being positive integers; determining a replacement object of the i-th inference service pod, in the case where it is determined that the i-th inference service pod is in an abnormal state based on the detection result of the i-th inference service pod; and processing the k-th channel of streaming media through the replacement object of the i-th inference service pod.

Abstract: Provided are a connection establishment method, a server, an accessed node, an access node, and a storage medium. The method includes: receiving a connection establishment request from an accessed node, establishing a connection to the accessed node, and acquiring accessed connection information of the accessed node; receiving an accessed address request from an access node and determining access connection information of the access node; sending the access connection information to the accessed node so that the accessed node opens up a connection channel between the access node and the accessed node according to the access connection information; and sending the accessed connection information of the accessed node to the access node so that the access node establishes a connection to the accessed node according to the accessed connection information in the case where the connection channel is opened up.

Abstract: A method and apparatus for processing an audio and video. The method includes: acquiring a target processing request including a target audio and video data stream; determining a target audio and video pipeline corresponding to the target processing request; the audio and video pipeline being constituted based on a plurality of functional components arranged in a chain structure, and the functional components being uniformly dispatched input data and recovered output data by a preset data stream dispatching module; and calling the target audio and video pipeline to continuously process the target audio and video data stream, and continuously outputting a processed audio and video data stream obtained after processing.

Abstract: A method and apparatus for sending a debugging instruction, an electronic device and a computer readable storage medium are provided. The method may include: after acquiring a debugging instruction sent by an operating terminal, determining a debugged terminal and a first edge communication node corresponding to the debugged terminal according to the debugging instruction, and determining a debugging communication link between the first edge communication node and the debugged terminal, the first edge communication node being determined based on first edge communication node information sent by the debugged terminal, and the first edge communication node information being determined and obtained based on an edge node computing application locally installed on the debugged terminal, and sending an debugging operation included in the debugging instruction to the debugged terminal through the debugging communication link.