Abstract: Embodiments of the present disclosure provide an amplitude and phase calibration method and device, a storage medium, and an electronic device. The method includes: receiving a plurality of signals to be calibrated, with the plurality of signals to be calibrated being signals of one or more digital channels; routing the plurality of signals to be calibrated to a plurality of basic units, with each of the basic units configured to process one of the signals to be calibrated; and performing amplitude and phase calibration on the plurality of signals to be calibrated by the plurality of basic units.

Abstract: This disclosure provides a video generation method, a video generation apparatus, an electronic device, a storage medium and a program product, and relates to the field of artificial intelligence technology, and in particular to the field of computer vision technology and deep learning technology. A specific implementation includes: obtaining document content information of a document; extracting, from the document content information, populating information for multiple scenes in a preset video template; populating the populating information for the multiple scenes into corresponding scenes in the preset video template, respectively, to obtain image information of the multiple scenes; generating audio information of the multiple scenes according to the populating information for the multiple scenes; generating a video of the document based on the image information and audio information of the multiple scenes.

Abstract: The present application provides a perovskite solar cell, including conductive glass, a hole transport layer, a perovskite layer, an electron transport layer and a back electrode, where a passivation layer may be disposed between the hole transport layer and the perovskite layer, and the passivation layer may include an amide and/or a cation thereof, where the amide may include a compound of formula (1) and/or formula (2): where R1 and R2 are each independently selected from hydrogen, —R, —NR2, —NHR, —NH2, —OH, —OR, —NHCOR, —OCOR, and —CH2COOH, where R represents a straight or branched chain alkyl group having 1-10 carbon atoms, m is an integer of 0 to 10; and n is an integer of 1 to 10; and where Ar is selected from a C5-C10 aryl or heteroaryl group.

Abstract: A method for processing a signal includes: in response to receiving an input feature map of the signal, dividing the input feature map into patches of a plurality of rows and patches of a plurality of columns, in which the input feature map represents features of the signal; selecting a row subset from the plurality of rows and a column subset from the plurality of columns, in which rows in the row subset are at least one row apart from each other, and columns in the column subset are at least one column apart from each other; and obtaining aggregated features by performing self-attention calculation on patches of the row subset and patches of the column subset.

Abstract: The present disclosure provides a method and apparatus for training a semantic segmentation model and a method and apparatus for performing a semantic segmentation on a video. The method comprises: acquiring a training sample set, wherein a training sample in the training sample set comprises at least one sample video stream and a pixel-level annotation result of the sample video stream; modeling a spatiotemporal context between video frames in the sample video stream using an initial semantic segmentation model to obtain a context representation of the sample video stream; calculating a temporal contrastive loss based on the context representation of the sample video stream and the pixel-level annotation result of the sample video stream; and updating a parameter of the initial semantic segmentation model based on the temporal contrastive loss to obtain a trained semantic segmentation model.

Abstract: Disclosed are a method for training a Student Network and a method for recognizing an image. The method includes: acquiring first prediction feature information of a sample image on the first granularity and second prediction feature information of the sample image on the second granularity by inputting the sample image into a Student Network, and acquiring first feature information of the sample image on the first granularity and second feature information of the sample image on the second granularity by inputting the sample image into a Teacher Network, and acquiring a target Student Network.

Abstract: This disclosure provides a video generation method, a video generation apparatus, an electronic device, a storage medium and a program product, and relates to the field of artificial intelligence technology, and in particular to the field of computer vision technology and deep learning technology. A specific implementation includes: obtaining document content information of a document; extracting, from the document content information, populating information for multiple scenes in a preset video template; populating the populating information for the multiple scenes into corresponding scenes in the preset video template, respectively, to obtain image information of the multiple scenes; generating audio information of the multiple scenes according to the populating information for the multiple scenes; generating a video of the document based on the image information and audio information of the multiple scenes.

Abstract: A method for extracting an iris from an image is presented. Edges of an iris are detected in an image. Texture from the image is acquired. The edges and texture are combined to generate an inner boundary and an outer boundary of the iris. Also, to improve the accuracy of the iris boundary detection, a method to select between ellipse and circle models is provided. Finally, a dome model is used to determine mask images and remove eyelid occlusions in unwrapped images.

Abstract: A method for extracting an iris from an image is presented. Edges of an iris are detected in an image. Texture from the image is acquired. The edges and texture are combined to generate an inner boundary and an outer boundary of the iris. Also, to improve the accuracy of the iris boundary detection, a method to select between ellipse and circle models is provided. Finally, a dome model is used to determine mask images and remove eyelid occlusions in unwrapped images.

Abstract: A method for filtering pixels is described. Filters including adjacent regions of pixels are defined and operators are associated with the regions. The operators are summation and subtraction operators. The filters have even and odd symmetry about a central axis. A total intensity value according to the application of the filter is determined. The total intensity value is binarized.

Abstract: A method for extracting features of an iris in an image is described. An unwrapped iris image is converted to an integral image by summations of pixel intensities. A novel bank of difference of sum filters is used to filter the integral image. The filtered output is binarized to produce an iris feature vector. The iris feature vector is used for iris matching.