Abstract: An image processing method includes detecting human skeleton key points of a character in an image, and for the to-be-processed image, generating a cropping region based on the human skeleton key points and a preset rule, and using the to-be-processed image selected by using the cropping region as an output object. The human skeleton key points and the preset rule are set based on photographing experience.

Abstract: In a method for selecting pictures from a sequence of pictures of an object in motion, a computerized user device determines, for each picture in the sequence of pictures, a value of a motion feature of the object. Based on analyzing the values of the motion feature of the pictures in the sequence, the device identifies a first subset of pictures from the pictures in the sequence. The device then selects, based on a second selection criterion, a second subset of pictures from the first subset of pictures. The pictures in the second subset are displayed to a user for further selection.

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.

Abstract: An instance segmentation method and apparatus are provided. A to-be-trained segmentation network performs the following processing on each instance group that is in a sample original image and that is of pixels of a marked instance, where each instance group includes at least one marked instance, and the processing includes: predicting at least two different first basic feature maps and a first attention feature map corresponding to each first basic feature map; performing weighted processing on the at least two first basic feature maps and pixel values of respective first attention feature maps corresponding to the at least two first basic feature maps, to obtain a first feature fusion map; and training the to-be-trained segmentation network based on the first feature fusion map and the sample original image. A segmentation model can precisely determine pixels of an instance in an original image.

Abstract: A multicore processor is provided. In order to select one of the multiple cores in such a multicore processor, an execution time of tasks which are performed multiple times is determined. Based on the determined execution time on the individual cores, an appropriate core for further executions of a task is selected. Additionally, the present disclosure further provides a code generator and code generating method for providing appropriate machine code for the multicore processor.

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 gesture recognition method, an electronic device, a computer-readable storage medium, and a chip, are provided, and relate to the field of artificial intelligence. The gesture recognition method includes: obtaining an image stream, and determining, based on a plurality of consecutive frames of hand images in the image stream, whether a user makes a preparatory action; when the user makes the preparatory action, continuing to obtain an image stream, and determining a gesture action of the user based on a plurality of consecutive frames of hand images in the continuously obtained image stream; and next, further responding to the gesture action to implement gesture interaction with the user. In this application, the preparatory action is determined before gesture recognition is performed, so that erroneous recognition occurring in a gesture recognition process can be reduced, thereby improving a gesture recognition effect.

Abstract: A method, apparatus, and computer program product for processing images by using a convolutional neural network (CNN) are proposed. An original image is received from an image source. The original image has a predefined size and high resolution, and is represented in a first color space supported by the image source. Then, an intermediate image is obtained by downscaling the original image in the first color space, and converted from the first color space to a second color space. Next, a restored image is obtained by upscaling the converted intermediate image to the predefined size of the original image. Said upscaling is performed by using the CNN on the original image and the converted intermediate image as inputs and return the restored image. The CNN is pre-trained on a set of triplets, comprising a past original image, a converted past intermediate image and a past restored image.

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: An image processing method. The method includes: An electronic device obtains N images, where the N images have a same quantity of pixels and a same pixel location arrangement, and N is an integer greater than 1; the electronic device obtains, based on feature values of pixels located at a same location in the N images, a reference value of the corresponding location; the electronic device determines a target pixel of each location based on a reference value of the location; and the electronic device generates a target image based on the target pixel of each location.

Abstract: This application discloses an image segmentation method in the field of artificial intelligence. The method includes: obtaining an input image and a processing requirement; performing multi-layer feature extraction on the input image to obtain a plurality of feature maps; downsampling the plurality of feature maps to obtain a plurality of feature maps with a reference resolution, where the reference resolution is less than a resolution of the input image; fusing the plurality of feature maps with the reference resolution to obtain at least one feature map group; upsampling the feature map group by using a transformation matrix W, to obtain a target feature map group; and performing target processing on the target feature map group based on the processing requirement to obtain a target image.

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: In a method for selecting pictures from a sequence of pictures of an object in motion, a computerized user device determines, for each picture in the sequence of pictures, a value of a motion feature of the object. Based on analyzing the values of the motion feature of the pictures in the sequence, the device identifies a first subset of pictures from the pictures in the sequence. The device then selects, based on a second selection criterion, a second subset of pictures from the first subset of pictures. The pictures in the second subset are displayed to a user for further selection.

Abstract: An image processing method includes detecting human skeleton key points of a character in an image, and for the to-be-processed image, generating a cropping region based on the human skeleton key points and a preset rule, and using the to-be-processed image selected by using the cropping region as an output object. The human skeleton key points and the preset rule are set based on photographing experience.

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.

Abstract: The present disclosure relates to a multicore processor. In order to select one of the multiple cores in such a multicore processor, an execution time of tasks which are performed multiple times is determined. Based on the determined execution time on the individual cores, an appropriate core for further executions of a task is selected. Additionally, the present disclosure further provides a code generator and code generating method for providing appropriate machine code for a multicore processor.

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: The present disclosure relates to a multicore processor. In order to select one of a multiplicity of cores in a multicore processor, an execution time of tasks which are performed multiple times is determined. Based on the determined execution time on the individual cores, an appropriate core for further executions of a task is selected. Additionally, the present disclosure further provides a code generator and code generating method for providing appropriate machine code for a multicore processor.

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 graph data query method and apparatus are disclosed, where the method includes: acquiring a partition number and a layer number of a query vertex; determining, based on the partition number and the layer number of the query vertex, a partition number and a layer number of a candidate vertex indicated by a query condition, and using the partition number and the layer number of the candidate vertex respectively as a candidate partition number and a candidate layer number; forming a candidate set using a vertex whose partition number and layer number satisfy any group of a candidate partition number and a candidate layer number; and performing graph data query in the candidate set according to the query condition.