Focuses on "feature alignment" using massive image-text pairs (e.g., LAION-5B). The goal is to teach the LLM what objects look like without updating the LLM weights.
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A lightweight MLP (Multi-Layer Perceptron) or a C-Abstractor that maps visual tokens into the language model's embedding space. 2. Training Methodology The model is typically trained in two distinct stages: A lightweight MLP (Multi-Layer Perceptron) or a C-Abstractor
Explaining complex scenes or reading text within images (OCR). The model is fine-tuned on high-quality
The model is fine-tuned on high-quality, multimodal instruction-following datasets (like LLaVA-Instruct). In this stage, both the projector and the LLM weights may be updated to handle conversational context. 3. Key Capabilities