A vision language model (VLM) is a type of artificial intelligence that can process both images and text. They are essentially a combination of two powerful AI techniques: computer vision (CV) and natural language processing (NLP).

Here's how it works: VLMs are trained on massive datasets of images and their corresponding text descriptions. This text data can be captions written by humans, machine-generated descriptions, or even simple labels. By analyzing these image-text pairs, the model learns to recognize the relationships between what it sees in the image and the words used to describe it.

VLMs can then perform various tasks, including:

• Image captioning: Generating descriptions for new images they've never seen before.

• Visual question answering: Answering questions about an image based on its content.

• Image recognition: Identifying objects and scenes within an image.

• Image retrieval: Finding images based on a text description.

A VLM (Vision Language Model) is a type of multimodal model, but not all multimodal models are VLMs.

Here's the breakdown:

• Multimodal model: This is a general term for any AI model that can process and understand data from two or more different modalities. These modalities could be text, images, audio, video, or even sensor data.

• Vision Language Model (VLM): This is a specific type of multimodal model that focuses on understanding the relationship between visual data (images) and textual data (language).

There are many other types of multimodal models that work with different combinations of modalities.

Here's an analogy: Think of multimodal models as a big umbrella, and VLMs are a specific type of umbrella designed for rain and sun (vision and language). There are other umbrellas under the big multimodal category that might be designed for wind and rain (audio and text) or heat and rain (thermal data and text).

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