unified vision-language models

**Unified Vision-Language Models** are **architectures designed to process and generate both visual and textual data** — tackling multiple tasks (VQA, captioning, retrieval, generation) within a single, cohesive framework rather than using separate specialized models. **What Are Unified VL Models?** - **Definition**: Models that jointly model $P(Image, Text)$. - **Trend**: Convergence of architecture (Transformer) and objective (Next Token Prediction / Masked Modeling). - **Examples**: BEiT-3, OFA (One For All), Unified-IO, Flamingo. - **Goal**: General-purpose intelligence that can perceive, reason, and communicate. **Key Approaches** - **Single-Stream**: Concatenate image patches and text tokens into one long sequence (e.g., UNITER). - **Dual-Stream**: Separate encoders with cross-attention layers (e.g., ALBEF). - **Encoder-Decoder**: Encode image, decode text (e.g., BLIP, CoCa). **Why They Matter** - **Parameter Efficiency**: One model weight file replaces dozens of task-specific models. - **Emergent Abilities**: Can reason about images in ways not explicitly trained (e.g., counting, logic). - **Simplification**: Drastically simplifies the AI deployment stack. **Unified VL Models** are **the foundation of Multimodal AI** — breaking down the silos between seeing and speaking to create truly perceptive artificial intelligence.