  In the realm of multi-modality, text-guided image retouching techniques
emerged with the advent of deep learning. Most currently available text-guided
methods, however, rely on object-level supervision to constrain the region that
may be modified. This not only makes it more challenging to develop these
algorithms, but it also limits how widely deep learning can be used for image
retouching. In this paper, we offer a text-guided mask-free image retouching
approach that yields consistent results to address this concern. In order to
perform image retouching without mask supervision, our technique can construct
plausible and edge-sharp masks based on the text for each object in the image.
Extensive experiments have shown that our method can produce high-quality,
accurate images based on spoken language. The source code will be released
soon.
