Abstract: Embodiments of the present disclosure disclose a method for processing tasks in parallel, a device and a storage medium, and relate to a field of artificial intelligent technologies. The method includes: determining at least one parallel computing graph of a target task; determining a parallel computing graph and an operator scheduling scheme based on a hardware execution cost of each operator task of each of the at least one parallel computing graph in a cluster, in which the cluster includes a plurality of nodes for executing the plurality of operator tasks, and each parallel computing graph corresponds to at least one operator scheduling scheme; and scheduling and executing the plurality of operator tasks of the determined parallel computing graph in the cluster based on the determined parallel computing graph and the determined operator scheduling scheme.

Abstract: The present disclosure provides a method for generating a backbone network, an apparatus for generating a backbone network, a device, and a storage medium. The method includes: acquiring a set of a training image, a set of an inference image, and a set of an initial backbone network; training and inferring, for each initial backbone network in the set of the initial backbone network, the initial backbone network by using the set of the training image and the set of the inference image, to obtain an inference time and an inference accuracy of a trained backbone network in an inference process; determining a basic backbone network based on the inference time and the inference accuracy of the trained backbone network in the inference process; and obtaining a target backbone network based on the basic backbone network and a preset target network.

Abstract: A method and apparatus for building a conversation understanding system based on artificial intelligence, a device and a computer-readable storage medium. In embodiments of the present disclosure, it is feasible to obtain the training feedback information provided by conversation service conducted by the user and the basic conversation understanding system, then according to the training feedback information, perform adjustment processing for a service state of the basic conversation understanding system, to obtain an adjustment state of the basic conversation understanding system. It is possible to perform data merging processing according to the training feedback information and the adjustment state of the basic conversation understanding system, to obtain model training data for building the model conversation understanding system.

Abstract: The present disclosure provides a method for training a feature extraction model, a method for classifying an image and related apparatuses, and relates to the field of artificial intelligence technology such as deep learning and image recognition. The scheme comprises: extracting an image feature of each sample image in a sample image set using a basic feature extraction module of an initial feature extraction model, to obtain an initial feature vector set; performing normalization processing on each initial feature vector in the initial feature vector set using a normalization processing module of the initial feature extraction model, to obtain each normalized feature vector; and guiding training for the initial feature extraction model through a preset high discriminative loss function, to obtain a target feature extraction model as a training result.

Abstract: A resource allocation method, including: determining a neural network model to be allocated resources, and determining a set of devices capable of providing resources for the neural network model; determining, based on the set of devices and the neural network model, first set of evaluation points including first number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme; updating and iterating first set of evaluation points to obtain second set of evaluation points including second number of evaluation points, each of which corresponds to one resource allocation scheme and resource use cost corresponding to the resource allocation scheme, and second number being greater than first number; and selecting a resource allocation scheme with minimum resource use cost from the second set of evaluation points as a resource allocation scheme for allocating resources to the neural network model.

Abstract: The present disclosure provides a vision processing and model training method, device, storage medium and program product. A specific implementation solution is as follows: establishing an image classification network with the same backbone network as the vision model, performing a self-monitoring training on the image classification network by using an unlabeled first data set; initializing a weight of a backbone network of the vision model according to a weight of a backbone network of the trained image classification network to obtain a pre-training model, the structure of the pre-training model being consistent with that of the vision model, and optimize the weight of the backbone network by using real data set in a current computer vision task scenario, so as to be more suitable for the current computer vision task; then, training the pre-training model by using a labeled second data set to obtain a trained vision model.

Abstract: A method for distributing network layers in a neural network model includes: acquiring a to-be-processed neural network model and a computing device set; generating a target number of distribution schemes according to network layers in the to-be-processed neural network model and computing devices in the computing device set, the distribution schemes including corresponding relationships between the network layers and the computing devices; according to device types of the computing devices, combining the network layers corresponding to the same device type in each distribution scheme into one stage, to obtain a combination result of each distribution scheme; obtaining an adaptive value of each distribution scheme according to the combination result of each distribution scheme; and determining a target distribution scheme from the distribution schemes according to respective adaptive value, and taking the target distribution scheme as a distribution result of the network layers in the to-be-processed neural n

Abstract: A model training system includes at least one first cluster and a second cluster communicating with the at least first cluster. The at least one first cluster is configured to acquire a sample data set, generate training data according to the sample data set, and send the training data to the second cluster; and the second cluster is configured to train a pre-trained model according to the training data sent by the at least one first cluster.

Abstract: Provided are an image processing method and apparatus, a device, a medium and a program product. The image processing method includes: performing image augmentation on an original image to obtain at least one augmented image; performing subject detection on the original image and the at least one augmented image to obtain an original detection frame in the original image and an augmented detection frame in the at least one augmented image; determining whether the original detection frame and the augmented detection frame belong to the same subject; and in response to the original detection frame and the augmented detection frame belonging to the same subject, determining a target subject frame in the original image according to the augmented detection frame.

Abstract: A method and apparatus is provided for generating and applying a deep learning model based on a deep learning framework, and relates to the field of computers. A specific implementation solution includes that a basic operating environment is established on a target device, where the basic operating environment is used for providing environment preparation for an overall generation process of a deep learning model; a basic function of the deep learning model is generated in the basic operating environment according to at least one of a service requirement and a hardware requirement, to obtain a first processing result; an extended function of the deep learning model is generated in the basic operating environment based on the first processing result, to obtain a second processing result; and a preset test script is used to perform function test on the second processing result, to output a test result.

Abstract: Provided are a text processing method, a device and a storage medium, relating to a field of computer technology, and especially to a field of artificial intelligence, such as natural language processing and deep learning. The specific implementation scheme includes: performing text processing on first text, by using a text processing acceleration operator; and processing, in parallel and faster, content after the text processing, by using the text processing acceleration operator. Text processing and parallel acceleration are carried out by the text processing acceleration operator, which can improve the speed of text processing.

Abstract: A distributed training method based on end-to-end adaption, a device and a storage medium. The method includes: obtaining slicing results by slicing a model to be trained; obtaining an attribute of computing resources allocated to the model for training by parsing the computing resources, in which the computing resources are determined based on a computing resource requirement of the model, computing resources occupied by another model being trained, and idle computing resources, and the attribute of the computing resources is configured to represent at least one of a topology relation and a task processing capability of the computing resources; determining a distribution strategy of each of the slicing results in the computing resources based on the attributes of the computing resources; and performing distributed training on the model using the computing resources based on the distribution strategy.

Abstract: A method for providing an intelligent service, an intelligent service system and an intelligent terminal based on artificial intelligence. The method comprises: receiving a first service request from a user (102); determining a search term and the weight thereof for the first service request (104); providing a first service result according to the search term and the weight thereof (106); and collecting feedback information for the first service result from the user, and adjusting, in real time, the search term and/or the weight thereof for the first service request, according to evaluation information in the feedback information (108).

Abstract: The disclosure provides a method for processing data, and an electronic device. The method includes: obtaining first attribute information of input data and second attribute information of a computing device corresponding to the input data; selecting a target operator implementation mode from a plurality of candidate operator implementation modes based on the first attribute information and the second attribute information; determining a plurality of sub-operators included in an operator required for the input data from an operator library based on the target operator implementation mode, to generate the operator; and obtaining an operation result by performing an operation on the input data by the computing device based on the operator.

Abstract: The disclosure provides an access method, an access apparatus, an electronic device and a computer storage medium, and relates to a field of computer technologies, in particular to a field of artificial intelligence technologies such as chip and deep learning. The method includes: determining a computational graph for calling an access device based on operator representations in a target model; optimizing the computational graph based on information of the access device; and performing relevant running operations of the target model on the access device based on the computational graph and an interface for the access device to access to a model framework of the target model, the interface being determined based on kit data of the access device.

Abstract: A method for detecting an object in an image includes: obtaining an image to be detected; generating a plurality of feature maps based on the image to be detected by a plurality of feature extracting networks in a neural network model trained for object detection, in which the plurality of feature extracting networks are connected sequentially, and input data of a latter feature extracting network in the plurality of feature extracting networks is based on output data and input data of a previous feature extracting network; and generating an object detection result based on the plurality of feature maps by an object detecting network in the neural network model.

Abstract: The present disclosure provides an image recognition method and apparatus, an electronic device and a readable storage medium, and relates to the field of artificial intelligence technologies, such as image processing and deep learning technologies. The image recognition method includes: acquiring a to-be-recognized image, and determining a to-be-recognized subject in the to-be-recognized image; extracting a subject feature of the to-be-recognized subject, and obtaining a target feature according to the subject feature; determining a target candidate feature in a plurality of candidate features using the target feature; and taking a class corresponding to the target candidate feature as a recognition result of the to-be-recognized subject. With the present disclosure, different image recognition requirements may be met, and a speed and accuracy of image recognition may be improved.

Abstract: The present disclosure provides an image processing method and apparatus, and relates to the field of image processing, and in particular to the field of image annotation. An implementation is: obtaining an image to be processed including a target region to be annotated; in response to a first click on the target region, performing a first operation to expand a predicted region for the target region based on a click position of the first click; in response to a second click in a position where the predicted region exceeds the target region, performing a second operation to reduce the predicted region based on a click position of the second click; and in response to determining that a difference between the predicted region and the target region meets a preset condition, obtaining an outline of the predicted region to annotate the target region.

Abstract: A method and apparatus of converting a schema in a deep learning framework, an electronic device, and a computer storage medium are provided. The method of converting the schema in the deep learning framework includes: updating a first schema, based on first syntax elements in the first schema and a context relationship between the first syntax elements in the first schema, so as to obtain an updated first schema; generating second syntax elements corresponding to updated first syntax elements in the updated first schema, based on a mapping relationship between the updated first syntax elements in the updated first schema and second syntax elements in a second schema system; and combining the second syntax elements according to a context relationship between the updated first syntax elements, so as to generate a second schema.

Abstract: A method for detecting an object in an image includes: obtaining an image to be detected; generating a plurality of feature maps based on the image to be detected by a plurality of feature extracting networks in a neural network model trained for object detection, in which the plurality of feature extracting networks are connected sequentially, and input data of a latter feature extracting network in the plurality of feature extracting networks is based on output data and input data of a previous feature extracting network; and generating an object detection result based on the plurality of feature maps by an object detecting network in the neural network model.