Multimodal Models

Entity Type: Glossary ID: multimodal-models

Definition: AI models capable of processing, understanding, and generating content across multiple modalities such as text, images, audio, and video. These models can perform tasks that require cross-modal understanding, such as image captioning, visual question answering, text-to-image generation, or audio-visual reasoning. Examples include GPT-4V, CLIP, DALL-E, and Flamingo, which demonstrate the ability to bridge different types of data representations.

Related Terms: - cross-modal-learning - vision-language-models - text-to-image - image-captioning - unified-models

Source Urls: - https://arxiv.org/abs/2103.00020 - https://openai.com/research/clip - https://arxiv.org/abs/2204.14198

Tags: - multimodal - vision-language - unified-models - cross-modal

Status: active

Version: 1.0.0

Created At: 2025-09-10

Last Updated: 2025-09-10