Multimodal Models

Mechanism

Use separate neural network components (e.g., image encoder for visual input, language model for text) to extract features, then fuse these representations (often via an attention mechanism or joint layers) into one combined understanding.

Definition

AI deep-learning models that simultaneously process different modalities, such as text, video, audio, and image, to generate outputs.

Core Function

Capable of understanding and processing virtually any input, combining different types of information, and generating almost any output.

Key Challenge

Building simple yet effective architectures to reduce training times and improve accuracy.

Distinction from LLMs

Process data from multiple data modalities, while Large Language Models (LLMs) only work with textual data.

Ongoing

Ongoing

Multimodal models are AIdeep-learning models that simultaneously process different modalities, such as text, video, audio, and image, to generate outputs. Multimodal frameworks contain mechanisms to integrate multimodal data collected from multiple sources for more context-specific and comprehensive understanding. [...] What are multimodal models?Multimodal models are AI algorithms that simultaneously process multiple data modalities such as text, image, video, and audio to generate more context-aware output. What is the difference between LMMs and LLMs?Large Multimodal Models (LMMs) process data from multiple data modalities, while Large Language Models (LLMs) only work with textual data. [...] Image 10: blog_image_12258 _Segmentation_ Multimodal models can help users perform segmentation more quickly by segmenting areas automatically based on textual prompts. For instance, users can ask the model to segment and label items in the image’s background. Top Multimodal Models Multimodal models are an active research area where experts build state-of-the-art frameworks to address complex issues using AI.

View more How It Works ### A multimodal model is capable of understanding and processing virtually any input, combining different types of information, and generating almost any output. For instance, usingVertex AI with Gemini, users can prompt with text, images, video, or code to generate different types of content than originally inputted. View documentation. Rapid Migration and Modernization Program End-to-end migration program to simplify your path to the cloud. [...] Multimodal AI and multimodal models represent a leap forward in how developers build and expand the functionality of AI in the next generation of applications. For example, Gemini can understand, explain and generate high-quality code in the world’s most popular programming languages, like Python, Java, C++, and Go—freeing developers to work on building more feature filled applications. Multimodal AI's potential also brings the world closer to AI that's less like smart software and more like an [...] New customers get up to $300 in free credits to try multimodal models in Vertex AI and other Google Cloud products. Try Vertex AI free model that is capable of processing information from different modalities, including images, videos, and text. For example, Google's multimodal model, Gemini, can receive a photo of a plate of cookies and generate a written recipe as a response and vice versa. Image 3: Multimodal AI Exploring multimodal AI with Gemini 5:14

As multimodal learning gains traction, many specialized datasets and model architectures are being introduced. Notable multimodal learning models include Flamingo and Stable Diffusion. Multimodal learning has various practical applications, including text-to-image generation, emotion recognition, and image captioning. This AI field has yet to overcome certain challenges, such as building simple yet effective architectures to reduce training times and improve accuracy. [...] Multimodal deep learning brings AI closer to human-like behavior by processing various modalities simultaneously. AI models can generate more accurate outcomes by integrating relevant contextual information from various data sources (text, audio, image). A multimodal model requires specialized embeddings and fusion modules to create representations of the different modalities. [...] Multimodal learning models can combine computer vision and NLP to link text descriptions to respective images. This ability helps with image retrieval in large databases, where users can input text prompts and retrieve matching images. For instance, OpenAI’s CLIP model provides a wide variety of image classification tasks using natural language text available on the internet. As a real-world example, many modern smartphones provide this feature where users can type prompts like “Trees” or

As an example, a multimodal model can receive a photo of a landscape as an input and generate a written summary of that place’s characteristics. Or, it could receive a written summary of a landscape and generate an image based on that description. This ability to work across multiple modalities gives these models powerful capabilities. [...] Multimodal models add a layer of complexity to large language models (LLMs), which are based on transformers, themselves built on an encoder-decoder architecture with an attention mechanism to efficiently process data. Multimodal AI uses data fusion techniques to integrate different modalities. This fusion can be described as early (when modalities are encoded into the model to create a common representation space) mid (when modalities are combined at different preprocessing stages) and late [...] My IBM Log in Subscribe # What is multimodal AI? ## Authors Cole Stryker Editorial Lead, AI Models ## What is multimodal AI? Multimodal AI refers to machine learning models capable of processing and integrating information from multiple modalities or types of data. These modalities can include text, images, audio, video and other forms of sensory input.