Match each vision-language fusion strategy to its defining property

All three strategies answer one question: how do the visual features from the encoder reach the language model? The defining property of each is *where* the image information enters. **Projection-adapter**, the LLaVA-style design, runs a small MLP that maps encoder outputs into the LLM's token space. The image then sits in the sequence as extra tokens. It is cheap to train because the language model barely changes, but every image spends sequence length, so many images inflate the context window. Cross-attention, the Flamingo-style design, keeps the language model's own text tokens and lets them attend to visual features through inserted layers. The visual features live outside the sequence, so image count is decoupled from sequence length — you can feed many images without filling the context. Unified early fusion, used by Fuyu and native models like GPT-4o, tokenizes image patches into the same stream from the start, with no separate encoder. This gives the tightest cross-modal mixing but needs heavy joint training. The fourth item, the vision encoder, is not a fusion strategy at all — it is the ViT that splits the image into patches and produces the embeddings the fusion strategy consumes.

Matching these correctly turns on **where the visual features enter the language model**, because that single placement decision sets each strategy's cost profile. The **projection-adapter** (LLaVA-style) maps encoder outputs through a small MLP into the LLM token space, so visual features become extra tokens inside the sequence. Its defining property is the tradeoff: cheap to train and simple, but each image consumes sequence length, so image count multiplies context. This is why it dominates open models yet struggles with many-image requests. Cross-attention (Flamingo-style) keeps the LLM's text tokens and inserts gated layers through which those tokens attend to visual features held outside the sequence. Its defining property is decoupling — sequence length no longer scales with image count, so you can feed many images, at the price of added parameters and harder training. Unified early fusion (Fuyu, native GPT-4o and Gemini) tokenizes image patches directly into one stream with no separate encoder. Its defining property is the tightest cross-modal mixing and low latency, paid for with heavy joint training from scratch. The fourth entry, the **vision encoder** (a ViT), is deliberately not a fusion strategy. It splits the image into patches and produces the embeddings that whichever fusion strategy then consumes. Keeping it distinct is the point of the question — the encoder produces features; the fusion strategy decides how they meet the language.