Abstract: Provided is a real scene image editing method based on hierarchically classified text guidance, including: firstly selecting a hierarchical multi-label text classification model and hierarchically classify an input style description text; obtaining a latent vector of an indoor scene image and dividing the latent vector; training latent space residual mappers which are divided into four groups for generating details of a layout, an object, an attribute, and a color in the scene image, and selectively training a mapping model with a secondary word obtained by a text classification model; inputting a tertiary word obtained by the text classification model to a contrastive language-image pre-training (CLIP) network and controlling training of the mapping network by utilizing a CLIP loss; hierarchically inputting the latent vector to the mapping network to obtain a bias vector, summing the bias vector with an original vector for inputting to the StyleGAN to obtain an edited image.

Abstract: Provided is a real scene image editing method based on hierarchically classified text guidance, including: firstly selecting a hierarchical multi-label text classification model and hierarchically classify an input style description text; obtaining a latent vector of an indoor scene image and dividing the latent vector; training latent space residual mappers which are divided into four groups for generating details of a layout, an object, an attribute, and a color in the scene image, and selectively training a mapping model with a secondary word obtained by a text classification model; inputting a tertiary word obtained by the text classification model to a contrastive language-image pre-training (CLIP) network and controlling training of the mapping network by utilizing a CLIP loss; hierarchically inputting the latent vector to the mapping network to obtain a bias vector, summing the bias vector with an original vector for inputting to the StyleGAN to obtain an edited image.