| Step | Data Format | Example Matrix Shape | Detailed Explanation |
|---|---|---|---|
| 1. Raw Image Input | Pixel Matrix | [1, 3, 224, 224] |
A single 224x224 3-channel (RGB) color image. |
| 2. ViT Patching | Patch Sequence | [1, 196, 768] |
The image is sliced into 196 patches of size 16x16 and flattened into vectors. |
| 3. ViT Output | Visual Feature Sequence | [1, 196, 768] |
Processed by the ViT encoder to obtain a feature sequence containing global information. |
| 4. Connector Projection | Aligned Visual Features | [1, 196, 4096] |
Visual features are projected into a 4096-dimensional space to align with the LLM's word embedding dimension. |
| 5. Text Embedding | Tokenized Text Features | [1, 5, 4096] |
The question "What is in the image?" is tokenized and embedded into five 4096-dimensional vectors. |
| 6. Multimodal Concatenation | Input Visual + Text Joint Features | [1, 201, 4096] |
Concatenating 196 visual tokens and 5 text tokens along the sequence dimension. |
| 7. LLM Generation Output | Generated Token ID Sequence | [1, 7] |
The LLM generates a 7-token answer based on the joint features. |
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翩若惊鸿_2026-01-02 21:51
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