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:
The present invention relates to a multicore processor 1. In order to select one of the multiple cores 21, 22, 23 in such a processor, an execution time of tasks which are performed multiple times is determined Based on the determined execution time on the individual cores 21, 22, 23, an appropriate core 21, 22, 23 for further executions of a task is selected. Additionally, the present invention further provides a code generator and code generating method for providing appropriate machine code for a multicore processor 1.
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 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:
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:
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:
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:
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:
Embodiments of the present invention provide an open application programming interface selection method and device. The method includes: receiving an invocation request from a user, where the invocation request includes an OpenAPI function parameter; determining an OpenAPI equivalent set according to the OpenAPI function parameter; and selecting a target OpenAPI from multiple OpenAPIs according to a Qos attribute value that corresponds to each OpenAPI in the OpenAPI equivalent set. By adopting the embodiments of the present invention, an OpenAPI with better performance can be selected from numerous OpenAPIs with equivalent functions for a user, thereby improving the quality of service for the user.