Abstract: The present disclosure relates to a network training method, an electronic device and a storage medium. The network training method includes the following steps. At least one implicit vector may be input into at least one pre-trained generative network to obtain a first generated image; the generative network may be obtained with a discriminative network through adversarial trainings with a plurality of natural images. A degradation process may be performed on the first generated image to obtain a first degraded image of the first generated image. The implicit vector and the generative network may be trained according to the first degraded image and a second degraded image of at least one target image; the trained generative network and the trained implicit vector may be used to generate at least one reconstructed image of the target image.

Abstract: A semantic segmentation model training method includes: performing, by a semantic segmentation model, image semantic segmentation on at least one unlabeled image to obtain a preliminary semantic segmentation result as the category of the unlabeled image; obtaining, by a convolutional neural network based on the category of the at least one unlabeled image and the category of at least one labeled image, sub-images respectively corresponding to the at least two images and features corresponding to the sub-images, where the at least two images comprise the at least one unlabeled image and the at least one labeled image, and the at least two sub-images carry the categories of the corresponding images; and training the semantic segmentation model on the basis of the categories of the at least two sub-images and feature distances between the at least two sub-images.

Abstract: An image processing method and a device, and a network training method and a device are provided. The image processing method includes determining a guide group arranged on an image to be processed and directed at a target object, the guide group comprising at least one guide point, and the guide point being used to indicate the position of a sampling pixel, and the magnitude and direction of the motion speed of the sampling pixel; and on the basis of the guide point in the guide group and the image to be processed, performing optical flow prediction to obtain the motion of the target object in the image to be processed.

Abstract: A semantic segmentation model training method includes: performing, by a semantic segmentation model, image semantic segmentation on at least one unlabeled image to obtain a preliminary semantic segmentation result as the category of the unlabeled image; obtaining, by a convolutional neural network based on the category of the at least one unlabeled image and the category of at least one labeled image, sub-images respectively corresponding to the at least two images and features corresponding to the sub-images, where the at least two images comprise the at least one unlabeled image and the at least one labeled image, and the at least two sub-images carry the categories of the corresponding images; and training the semantic segmentation model on the basis of the categories of the at least two sub-images and feature distances between the at least two sub-images.