Review Neural Networks about Image Transformation Based on IGC Learning Framework with Annotated Information
It provides a structured review for researchers in computer vision, but it is incremental as it organizes existing work rather than introducing new methods.
This paper tackles the lack of a unified framework for reviewing image transformation tasks in computer vision by proposing the IGC learning framework, which categorizes subtasks like image-to-image translation and style transfer into Independent, Guided, and Cooperative learning, and includes experiments to verify its effectiveness.
Image transformation, a class of vision and graphics problems whose goal is to learn the mapping between an input image and an output image, develops rapidly in the context of deep neural networks. In Computer Vision (CV), many problems can be regarded as the image transformation task, e.g., semantic segmentation and style transfer. These works have different topics and motivations, making the image transformation task flourishing. Some surveys only review the research on style transfer or image-to-image translation, all of which are just a branch of image transformation. However, none of the surveys summarize those works together in a unified framework to our best knowledge. This paper proposes a novel learning framework including Independent learning, Guided learning, and Cooperative learning, called the IGC learning framework. The image transformation we discuss mainly involves the general image-to-image translation and style transfer about deep neural networks. From the perspective of this framework, we review those subtasks and give a unified interpretation of various scenarios. We categorize related subtasks about the image transformation according to similar development trends. Furthermore, experiments have been performed to verify the effectiveness of IGC learning. Finally, new research directions and open problems are discussed for future research.