Bidirectional Multimodal Transformation Without Model Switching

via “bidirectional text-to-image and image-to-text generation with unified token representation”

* ⏫ 07/2023: [Meta-Transformer: A Unified Framework for Multimodal Learning (Meta-Transformer)](https://arxiv.org/abs/2307.10802)

Unique: Uses a single decoder-only transformer with unified token representation for both modalities rather than separate vision encoders and text decoders, eliminating the need for cross-modal fusion layers and enabling true bidirectional generation through standard autoregressive training

More parameter-efficient than encoder-decoder multimodal models (CLIP, BLIP) because it eliminates separate vision encoders; achieves 5x better training efficiency than comparable text-to-image methods while maintaining competitive zero-shot quality