Abstract: A data processing method, a data processing apparatus, an electronic device, and a computer-readable storage medium are provided. The data processing method includes: in response to an operation of importing a resource file of an object to be tried on, performing processing on the resource file and obtaining a target file in a file format adapted to an effect editor; adding a rendering component to a preset node of the effect editor in response to an operation of dragging the target file to the preset node; and rendering, based on the rendering component, a try-on image in which a model of the object to be tried on is tried on a model of a preset part.

Abstract: This application provides a communication method and apparatus. The communication method includes: receiving measurement configuration information from a network device, where the measurement configuration information is used to configure a channel measurement resource and M interference measurement resources, and M is a positive integer greater than or equal to 1; and sending a bitmap to the network device, where the bitmap includes one or more bits, each bit corresponds to one or more interference measurement resources and has a value indicating interference of the one or more interference measurement resources to the channel measurement resource, and the M interference measurement resources include the one or more interference measurement resources.

Abstract: A nanoscale positioning apparatus with a large stroke and multiple degrees of freedom and a control method thereof are provided. The nanoscale positioning apparatus includes a base, a plurality of parallel branch chain mechanisms and a working table. Each of the parallel branch chain mechanisms includes an electric cylinder, a micro-motion drive mechanism, a laser interferometer, a grating measuring device, a self-locking upper hinge and a self-locking lower hinge. The top of the base is connected to one end of the electric cylinder through the self-locking lower hinge. The other end of the electric cylinder is connected to one end of the micro-motion drive mechanism. The other end of the micro-motion drive mechanism is connected to the bottom of the working table through the self-locking upper hinge. The positioning apparatus has multiple degrees of freedom, and realizes multi-degree-of-freedom arbitrary position adjustment of the working table through parallel branch chain mechanisms.

Abstract: A nanoscale positioning apparatus with a large stroke and multiple degrees of freedom and a control method thereof are provided. The nanoscale positioning apparatus includes a base, a plurality of parallel branch chain mechanisms and a working table. Each of the parallel branch chain mechanisms includes an electric cylinder, a micro-motion drive mechanism, a laser interferometer, a grating measuring device, a self-locking upper hinge and a self-locking lower hinge. The top of the base is connected to one end of the electric cylinder through the self-locking lower hinge. The other end of the electric cylinder is connected to one end of the micro-motion drive mechanism. The other end of the micro-motion drive mechanism is connected to the bottom of the working table through the self-locking upper hinge. The positioning apparatus has multiple degrees of freedom, and realizes multi-degree-of-freedom arbitrary position adjustment of the working table through parallel branch chain mechanisms.

Abstract: A processing apparatus includes a collection module and a training module, the training module includes a backbone network and a region proposal network (RPN) layer, the backbone network is connected to the RPN layer, and the RPN layer includes a class activation map (CAM) unit. The collection module is configured to obtain an image, where the image includes an image with an instance-level label and an image with an image-level label. The backbone network is used to output a feature map of the image based on the image obtained by the collection module.

Abstract: Embodiments of the present invention provide a machine learning model training method, including: obtaining target task training data and N categories of support task training data; inputting the target task training data and the N categories of support task training data into a memory model to obtain target task training feature data and N categories of support task training feature data; training the target task model based on the target task training feature data and obtaining a first loss of the target task model, and separately training respectively corresponding support task models based on the N categories of support task training feature data and obtaining respective second losses of the N support task models; and updating the memory model, the target task model, and the N support task models based on the first loss and the respective second losses of the N support task models.