Residual Decoder Adapter: ID-Preserving Tokenizer Adaption for Autoregressive Text Rendering
Researchers have developed a Residual Decoder Adapter (RDA) to improve the text rendering capabilities of autoregressive visual models without retraining the entire system. The RDA works by refining the output of an existing visual tokenizer using a paired codebook and a parallel branch that learns residual differences. This approach significantly enhances text rendering accuracy, as demonstrated by a substantial increase in OCR accuracy on benchmarks like TextVisionBlend and StyledTextSynth. AI
IMPACT Enhances text rendering in autoregressive models, potentially improving OCR and text-based image generation applications.