How Early Fusion enables cross-modal attention through interleaving of text and vision tokens.
Step 5/5 in Chapter 2 "Modern Architecture Variants"
Practical application of Early Fusion. Shows how real conversations with multiple images work.
Claude, GPT-4o, Gemini support interleaved inputs. This enables true multi-image reasoning — e.g., "compare image 1 and image 3" in a conversation.
Problem: Vision and text are processed sequentially. Vision information cannot be directly integrated into text attention. Result: Weak connection between modalities.
Advantage: Joint processing enables direct cross-modal communication. Text queries can directly query vision tokens and vice versa.
Early Fusion allows the model to process text and vision information in the same attention space. Text tokens can directly query vision features without an external fusion layer.
When text says "the image shows X", the model can immediately check vision tokens. No detours via separate encoders. Result: +20-30% better accuracy on Visual QA tasks.
Transformer blocks are shared for all modalities. Instead of two separate encoders (Vision + Text) there's one unified encoder. Saves ~40% parameter requirements.
Llama 4, Qwen 3, Gemini 3 use Early Fusion. DeepSeek-V3 stays with Late Fusion, but loses performance on complex reasoning tasks. Industry consensus: Early Fusion becomes standard.
Interleaved Context enables multi-image scenarios. 3-5 images can be seamlessly mixed with text, without attention overhead. Long context (1M tokens) becomes practical with multiple images.
Benchmark results: MMVP +8%, MMBench +12%, ChartQA +15%. The biggest gains on reasoning tasks (understanding diagrams, recognizing relations). Pure image recognition benefits less.