2 results about "Random text" patented technology

The embodiment of the application provides an image generation method, a model training method and a text bolding method, and belongs to the technical field of image processing. The image generation method comprises the following steps: randomly selecting one from a plurality of preset image sizes as the size of a first image, and generating the first image containing random text; performing bolding on the random text in the first image based on preset bolding parameters to obtain a second image; wherein the preset bolding parameters comprise a preset bolding direction and a preset bolding value; and zooming the first image and the second image to a target image size. The pre-set deep learning network model is trained by using the processed first image and second image, which can improve the generalization ability of the model and ensure the readability and aesthetic degree of the text in the screen content image output by the model.

This invention discloses a zero-shot adversarial robust model, method, and computer device based on text information enhancement. First, text descriptions and random text are generated to enhance semantic expression diversity. Features of images and different texts are extracted using a visual language model. Adversarial examples are iteratively generated using image features and text description features, and used for adversarial training. Cross-entropy loss is calculated using the image features and text description features of the adversarial examples to train the adversarial model. The cosine similarity between random text features, adversarial example features, and clean example features is calculated separately, and the two are aligned using a loss. To maintain zero-shot performance on clean examples, the cosine similarity between random text features and clean example features in the original and target models is calculated, and the two are aligned using a loss. The three losses are integrated to obtain a zero-shot adversarial robust model based on text information enhancement. This method can improve the adversarial robustness and generalization performance of the model under zero-shot conditions.