Unified Pix Token And Word Token Generative Language Model
Since the emergence of Vision Transformer (ViT), it has been widely used in generative language model and generative visual model. Especially in the current state-of-art open source multimodal models, ViT obtained by CLIP or SigLIP method serves as the vision encoder backbone to help them acquire visual understanding capabilities. But this method leads to limitations in visual understanding for details, such as difficulty in recognizing small text or numbers in images. To address these issues, we propose a new model to unify pix token and word token into the generative language model. The new model also features with each pix of image having its own token embedding, color folding, global ...
| Authors | Haun Leung, ZiNan Wang |
|---|---|
| Primary category | cs.CV |
| Published | 13 May 2026 |
| Last updated at source | 4 June 2026 |
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