A method includes: performing a voxelization processing on point cloud data to obtain a plurality of voxels, wherein the plurality of voxels correspond to a plurality of points in the point cloud data, and at least a portion of the plurality of voxels forms a voxel set; extracting a plurality of voxel features from the voxels in the voxel set; mapping the plurality of voxel features to a plurality of points included in the plurality of voxels, respectively, to obtain a plurality of point features of the plurality of points; and predicting, according to the plurality of point features, the target object.

Example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, techniques to process image signal values sampled from a multi color channel imaging device. In particular, methods and/or techniques disclosed herein are directed to synthesizing a temporally upsampled image frame to be in a temporal sequence of images frames.

Systems and methods are provided for computer aided phenotyping of biological samples with fibrillar structures. A digital image indicates presence of proteins or cells that can form fibrillar structures in the biological tissue sample. The image is processed to quantify parameters, each parameter describing a feature of the proteins or cells fibers that is expected to be different for different phenotypes of interest of their structure. At least some features are tissue level features that describe macroscopic characteristics, morphometric level features that describe morphometric characteristics of the fibrillar structures, and texture level features that describe an organization of the fibrillar structures.

A parameter processing method includes playing a video for a second account, wherein a first account controls a first virtual object in the virtual scene in the video, and displaying a first parameter policy set associated with the video. The first parameter policy set includes at least one of (i) position, size, or function of keys for controlling the first virtual object or (ii) sensitivity of virtual props in the virtual scene. The method further includes updating a second parameter policy set corresponding to a second account in response to a parameter policy update operation. Updated parameters of the second parameter policy set include at least one parameter that is the same as a parameter of the first parameter policy set.

A method for small object detection in drone scene based on deep learning is provided, which includes: inputting images captured by a drone into a pre-trained generator based on an Unet network structure to output normal-light images; inputting the normal-light images into a object detection backbone network to output a plurality of multidimensional matrix feature maps, wherein the object detection backbone network integrates a channel attention mechanism and a spatial attention mechanism based on convolutional block Self-Block, and a 7*7 large convolutional kernel is used; inputting the plurality of multidimensional matrix feature maps into a BiFPN-S module of a feature pyramid for feature fusion, so as to output a plurality of corresponding feature maps for predicting objects of different sizes.

A fingerprint anti-counterfeiting method and an electronic device are provided. The fingerprint anti-counterfeiting method includes: After detecting a fingerprint input action of a user, an electronic device obtains a fingerprint image generated by the fingerprint input action, and obtains a vibration-sound signal generated by the fingerprint input action. The device determines, based on a fingerprint anti-counterfeiting model, whether the fingerprint input action is performed by a true finger. The fingerprint anti-counterfeiting model is a multi-dimensional network model obtained through learning based on fingerprint images for training and corresponding vibration-sound signals. The fingerprint anti-counterfeiting method in embodiments of this application helps improve a protection capability of the electronic device for a fake fingerprint attack.

A method for detecting a face synthetic image, an electronic device and a storage medium are provided. The technical solution includes inputting a face image to be detected into a pre-trained convolution neural network to obtain a raw image feature of the face image; inputting the raw image feature into a first full connected layer and a second full connected layer respectively to obtain a first feature vector corresponding to a face key point of the face image and a second feature vector corresponding to the face image; merging the first feature vector and the second feature vector to obtain a merged feature vector; inputting the merged feature vector to a third full connected layer to obtain a detection result of the face image.

A face search method and apparatus are provided. The method includes obtaining a to-be-searched face image, and inputting the face image into a first feature extraction model to obtain a first face feature. The method further includes inputting the face image and the first face feature into a first feature mapping model for feature mapping, to output a standard feature corresponding to the first face feature, and performing face search for the face image based on the standard feature. Features extracted by using a plurality of feature extraction models are concatenated, and a concatenated feature is used as a basis for constructing a standard feature.

In one aspect, an example method includes (i) extracting a sequence of audio features from a portion of a sequence of media content; (ii) extracting a sequence of video features from the portion of the sequence of media content; (iii) providing the sequence of audio features and the sequence of video features as an input to a transition detector neural network that is configured to classify whether or not a given input includes a transition between different content segments; (iv) obtaining from the transition detector neural network classification data corresponding to the input; (v) determining that the classification data is indicative of a transition between different content segments; and (vi) based on determining that the classification data is indicative of a transition between different content segments, outputting transition data indicating that the portion of the sequence of media content includes a transition between different content segments.

A system and method to perform detection based on sensor fusion includes obtaining data from two or more sensors of different types. The method also includes extracting features from the data from the two or more sensors and processing the features to obtain a vector associated with each of the two or more sensors. The method further includes concatenating the two or more vectors obtained from the two or more sensors to obtain a fused vector, and performing the detection based on the fused vector.