Unsupervised SIFT features-to-Image Translation using CycleGAN
Sławomir Maćkowiak, Patryk Brudz, Mikołaj Ciesielsk, Maciej Wawrzyniak · Computer Science Research Notes · 2021
The generation of video content from a small set of data representing the features of objects has very promising application prospects. This is particularly important in the context of the work of the MPEG Video Coding for Machine group, where various efforts are being undertaken related to efficient image coding for machines and humans. The representation of feature points well understood by machines in a video form, which is easy to understand by humans, is an important current challenge. This paper presents results on the ability to generate images from a set of SIFT feature points without descriptors using the generative adversarial network CycleGAN. The impact of the SIFT keypoint representation method on the learning quality of the network is presented. The results and a subjective evaluation of the generated images are presented.