Principle:Mit han lab Llm awq Multimodal Vision Language Inference
| Knowledge Sources | |
|---|---|
| Domains | Multimodal, Deep_Learning |
| Last Updated | 2026-02-15 00:00 GMT |
Overview
Principle of combining a vision encoder with a language model to perform multimodal inference where image features are fused with text token embeddings.
Description
Multimodal vision-language inference involves three stages: (1) extracting visual features from images using a vision encoder, (2) projecting those features into the language model's embedding space via an MLP bridge, and (3) replacing placeholder image tokens in the text sequence with the projected visual features before running the language model. This enables the LLM to see the image content and generate text conditioned on it. The approach supports streaming generation, quantized inference, and both image and video inputs.
Usage
Apply this principle when deploying a model that must understand images or videos and generate natural language responses about them.
Theoretical Basis
The fusion mechanism replaces special image placeholder tokens in the text embedding sequence with projected visual features:
Pseudo-code:
# Abstract algorithm (NOT real implementation)
visual_features = project(vision_encoder(image))
text_embeddings = embed(input_ids)
text_embeddings[image_positions] = visual_features
output = language_model(text_embeddings)