In-Context Learning with Images is a capability of Multimodal LLMs to perform new tasks at inference time — by observing a few visual examples (demonstrations) provided in the prompt, without any weight updates or fine-tuning.

What Is Multimodal In-Context Learning?

• Definition: The ability to generalize from specific visual examples provided in the context window.
• Pattern: Prompt = "Image A: Label A. Image B: Label B. Image C: ?" -> Model predicts "Label C".
• Mechanism: The model attends to the interleaved image-text sequence to infer the underlying pattern or task.
• Requirement: Needs models trained on interleaved data (like Flamingo, Otter, or GPT-4V).

Why It Matters

• Adaptability: Users can customize model behavior on the fly (e.g., "Here is a defect, here is a clean chip. Classify this one.").
• Efficiency: No need for expensive retraining or fine-tuning pipelines.
• One-Shot Learning: Can often work with just a single example.

Applications

• Custom Classification: Teaching the model a new object category instantly.
• Visual Formatting: "Extract data from this invoice like this: {JSON example}".
• Style Transfer: "Describe this image in the style of this other caption."

In-Context Learning with Images is the hallmark of true visual intelligence — transforming models from static classifiers into flexible, adaptive reasoners.