Abstract:
A method for optimizing a photographing pose of a user, where the method is applied to an electronic device, and the method includes: displaying a photographing interface of a camera of the electronic device; obtaining a to-be-taken image in the photographing interface; determining, based on the to-be-taken image, that the photographing interface includes a portrait; entering a pose recommendation mode; and presenting a recommended human pose picture to a user in a predetermined preview manner, where the human pose picture is at least one picture that is selected from a picture library through metric learning and that has a top-ranked similarity to the to-be-taken image, and where the similarity is an overall similarity obtained by fusing a background similarity and a foreground similarity.
Abstract:
This application discloses an object recognition method and apparatus in the field of artificial intelligence. This application relates to the field of artificial intelligence, and specifically, to the field of computer vision. The method includes: obtaining one or more body regions of a to-be-recognized image; determining a saliency score of each of the one or more body regions; and when a saliency score of a body region A is greater than or equal to a categorization threshold, determining a feature vector of an object in the body region A based on a feature of the object in the body region A, and determining a category of the object in the body region A based on the feature vector of the object in the body region A and a category feature vector in a feature library, where the body region A is any one of the one or more body regions.
Abstract:
A classification model training method includes obtaining a positive training set and a first negative training set, where the positive training set includes samples of a positive sample set in a corpus, where the first negative training set includes samples of an unlabeled sample set in the corpus, training, using the positive training set and the first negative training set, to obtain a first classification model, determining, using the first classification model, a pseudo negative sample in the first negative training set, removing the pseudo negative sample from the first negative training set, updating the first negative training set to a second negative training set, and training, using the positive training set and the second negative training set, to obtain a target classification model.
Abstract:
A method and a related device for determining a management mode of a shared virtual memory page are disclosed. In one example, a method is disclosed that includes monitoring frequency or mode of access operation of at least one process accessing the shared virtual memory page; and changing the management mode of the shared virtual memory page to a shared physical memory mode if the monitored frequency or mode of access operation meets a first set condition and a current management mode of the shared virtual memory page is a distributed shared memory mode. The technical solutions provided in the present disclosure can enhance performance of accessing a shared virtual memory.
Abstract:
A data processing system, a computing node, and a data processing method are provided. The data processing system includes a management node and a first class of computing nodes. The management node is configured to allocate first processing tasks to the first class of computing nodes. At least two computing nodes in the first class of computing nodes concurrently perform the first processing tasks allocated by the management node. A computing node performs a combine2 operation and a reduce2 operation on a data block Mx and a data block V1x, to obtain a first intermediate result. Then, the management node obtains a processing result for a to-be-processed dataset according to first intermediate results obtained by the first class of computing nodes. According to the data processing system, when a combine operation and a reduce operation are being performed on data blocks, memory space occupied by computation can be reduced.
Abstract:
A code generating method, a compiler, a scheduling method, an apparatus and a scheduling system where the generated code is an executable code and applied to a heterogeneous system, and the heterogeneous system includes an accelerated processor and a central processing unit (CPU) and the code generating method includes acquiring, by a compiler, resource information of the accelerated processor and resource information of the CPU in order to generate an operable platform list, identifying, by the compiler, accelerable code from first user code, embedding, by the compiler, a hook function and an exception handling function before the accelerable code to form second user code, and compiling, by the compiler, the second user code to obtain the executable code and the executable code generated may automatically implement proper scheduling of processors during execution.
Abstract:
Embodiments of the present invention relate to a breakpoint information management method and a breakpoint information manager. The breakpoint information management method includes: obtaining, according to a query parameter obtained from a current application program and stored breakpoint information of at least one application program, breakpoint context information corresponding to the query parameter; calculating interest information according to the breakpoint context information; and returning the interest information to the current application program so that the current application program plays a corresponding electronic file. With the breakpoint information management method and the breakpoint information manager provided in the present invention, breakpoint information is deeply analyzed to obtain interest information, so that the breakpoint information is reused; and electronic files satisfying a user's interest characteristics are played for the user according to the interest information, thereby enlarging the application scope of the breakpoint information.
Abstract:
A method for optimizing a photographing pose of a user, where the method is applied to an electronic device, and the method includes: displaying a photographing interface of a camera of the electronic device; obtaining a to-be-taken image in the photographing interface; determining, based on the to-be-taken image, that the photographing interface includes a portrait; entering a pose recommendation mode; and presenting a recommended human pose picture to a user in a predetermined preview manner, where the human pose picture is at least one picture that is selected from a picture library through metric learning and that has a top-ranked similarity to the to-be-taken image, and where the similarity is an overall similarity obtained by fusing a background similarity and a foreground similarity.
Abstract:
A method for optimizing a photographing pose of a user, where the method is applied to an electronic device, and the method includes: displaying a photographing interface of a camera of the electronic device; obtaining a to-be-taken image in the photographing interface; determining, based on the to-be-taken image, that the photographing interface includes a portrait; entering a pose recommendation mode; and presenting a recommended human pose picture to a user in a predetermined preview manner, where the human pose picture is at least one picture that is selected from a picture library through metric learning and that has a top-ranked similarity to the to-be-taken image, and where the similarity is an overall similarity obtained by fusing a background similarity and a foreground similarity.
Abstract:
An image processing apparatus and a method are provided. The apparatus comprises a plurality of processing modules configured to operate in series to refine a raw image captured by a camera, the modules comprising a first module and a second module, each of which independently implements a respective trained artificial intelligence model, wherein: the first module implements an image transformation operation that performs an operation from the set comprising: (i) an essentially pixel-level operation that increases sharpness of an image input to the module, (ii) an essentially pixel-level operation that decreases sharpness of an image input to the module, (iii) an essentially pixel-block-level operation on an image input to the module; and the second module as a whole implements a different operation from the said set.