Text-Guided Mask-free Local Image Retouching
This addresses a limitation in deep learning for image retouching by making it more accessible and widely applicable without mask constraints.
The paper tackles the problem of text-guided image retouching by eliminating the need for object-level mask supervision, resulting in a method that generates high-quality, accurate images based on text descriptions.
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.