Unleashing In-context Learning of Autoregressive Models for Few-shot
Image Manipulation
Unleashing In-context Learning of Autoregressive Models for Few-shot
Image Manipulation
Text-guided image manipulation has experienced notable advancement in recent years. In order to mitigate linguistic ambiguity, few-shot learning with visual examples has been applied for instructions that are underrepresented in the training set, or difficult to describe purely in language. However, learning from visual prompts requires strong reasoning capability, which …