Abstract:Transformer architectures show spectacular performance on NLP tasks and have recently also been used for tasks such as image completion or image classification. Here we propose to use a sequential image representation, where each prefix of the complete sequence describes the whole image at reduced resolution. Using such Fourier Domain Encodings (FDEs), an autoregressive image completion task is equivalent to predicting a higher resolution output given a low-resolution input. Additionally, we show that an encod… Show more
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