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Multimodal Models: Understanding Their Significance in AI Systems

March 25, 2024
min read

The continual advancements in AI technology have enabled enterprises to imbue high value in their offerings and augment business decisions with data-backed insight. Generative AI is testimony to the usefulness of AI systems that can process large amounts of data to produce actionable insight.

The latest advancements have created multimodal models – a type of AI system that “sees” and processes information as humans do. By combining the power of generative AI and computer vision, multimodal models are paving the way for highly advanced applications of an ingenious AI system.

Let’s explore the significance of multimodal models by understanding what they are, how they work, and the fields they can be applied to.

Understanding Multimodal Models

To create a better understanding of multimodal models, let’s draw an analogy of how humans process information. The primary sensory input for humans is vision, which is augmented by sound, smell, touch, taste, and prior knowledge.

When the same learning concept is applied to AI systems, a multimodal model is born. Multimodal models combine different types of input information (modes) to generate a holistic picture of the current scenario.

For example, in addition to drawing insight from historical data and associated text inputs, a multimodal model can also apply learning from visual information (images, videos, etc.) to generate accurate and comprehensive insight on a subject.

There is one major difference between the traditional single-mode AI and a multimodal model: the source of information. Traditional single-mode AI models are trained on a specific type of data to form a baseline knowledge that the systems keep updating with the same kind of data periodically.

On the other hand, multimodal AI functions on multiple types of data inputs at the same time, drawing insight from each one and collating relevant information into usable insight to deliver a comprehensive result.

For example, a traditional AI model for financial insight would draw from business data and economic and industry data to generate insight. A multimodal financial model would augment this insight with information drawn from speech, audio, images, video, and other types of data as well.

In short, a multimodal AI model is capable of “seeing” and “processing” information in a way that is similar to humans.

Significance of Multimodal Data

Leveraging multimodal inputs for generative AI has tremendous potential to elevate the functioning of AI systems. While traditional AI systems have been providing robust augmentative and supportive functions to users, multimodal models have the potential to offer creativity and innovation to operations autonomously.

Multimodal datasets consist of different types of data, like images, video, audio, and text. By combining a large variety of data together, a machine learning model is capacitated to comprehend the holistic nature of information that is fed into it for processing.

A good example is applying an AI algorithm for analyzing human emotion. An unimodal model only has access to one type of dataset – a photograph, for example. Based on this limited information, an AI model can only “guess” what the person in the picture might be feeling.

On the other hand, a multimodal input would include a recorded audio from the same person in addition to the picture. The AI system would then work through the expressions in the picture and correlate and cross-check them with the audio information pertaining to the tone and pitch of the voice.

It will then be able to deliver a more comprehensive analysis of what the person might be feeling.

Multimodal data equips an AI system to interpret a dataset more holistically in a multilayered fashion to deliver sounder and more reliable insights.

Key Multimodal Models in Detail

There are three key types of multimodal models used today:

1. Transformer-based Multimodal Models

In machine learning, transformers are a type of AI algorithm that derive context and relevance from sequential data using a mechanism called “self-attention.”

This enables them to understand long-range dependencies between data points in a sequence, which makes them an excellent tool to apply for tasks such as machine translations, sentiment analysis, summarization of documents, and natural language understanding.

Applying transformers to multimodal AI tremendously improves the understanding capabilities of the AI system.

Transformer-based Multimodal Models

Think of the task of translation, for example: spoken words are not the only medium humans use to establish communication. Visual and aural cues such as body language, tone, and pitch of voice also add context to the translation.

Using multimodal transformers, AI systems can extend their understanding and translation capabilities beyond spoken words to deliver a more comprehensive interpretation in one or more languages.

Multimodal transformers work on the concept of encoding different modalities separately to combine them together for output later.

It would first encode the text string for translation using a standard transformer model and then proceed to leverage convolutional neural networks to encode the associated visual with the text.

It will then combine the two encodings using a fusion mechanism (like concatenation) and push it for final processing.

2. Fusion-based Model

Multimodal fusion is the method of AI interpretation that combines the gleaned information from diverse modalities into one comprehensive output with holistic considerations for all inputs.

The model basically integrates the data it receives from inputs like text, video, images, audio, etc., to generate an accurate representation of a situation and deliver results relevant to the queries.

Fusion-based Model

The biggest benefit that the multimodal fusion model provides is the capability to capitalize on the strength of diverse modes while addressing the limitations of each.

The information gaps created by one mode of data are filled by inputs through the second mode of data. It delivers stunning depth of insight and detail into patterns, trends, and relationships while understanding datasets.

Consider this model as assembling a puzzle with thousands of pieces – the complete picture emerges only when all the information gaps are filled with a variety of inputs.

The multimodal fusion model adopts four unique kinds of approaches:

  • Early fusion: This approach combines the multimodal data before it goes into the model.
  • Late fusion: This approach processes each modal data individually and combines it before output.
  • Intermediate fusion: In this approach, the process of combining and interpreting the input data happens at various stages within the model.
  • Hybrid fusion: In order to achieve highly tuned results, hybrid fusion is used to combine a variety of fusion strategies.

3. Graph-based Multimodal Models

Graph-based multimodal models are capable of learning from graphically represented information.

The benefit of using graph-based multimodal models is that they are able to address the challenges of introducing biases by establishing geometric relationships between cross-modal dependencies to combine them.

The multimodal architecture that these graphs are put into is image-sensitive, language-sensitive, and knowledge-grounded. The deep learning methods based on graphs have enabled breakthroughs in the use of neural networks for biology, chemistry, and physics.

Graph-based multimodal models enable measuring and modeling complex AI systems that require observation from different perspectives and scales. This capability is invaluable for mobilizing machine learning tasks such as relation extraction, summarization, image restoration, visual reasoning, and image classification.

Graph-based Multimodal Models

Graph-based multimodal models are especially helpful in situations where there are limitations in data collection. For multimodal models, every object must exist across all types of modalities, but this is not the case every time.

Since there is variation in modalities, it leads to an intricate relational dependency in the models, which cannot be leveraged effectively using simple modality fusion techniques that multimodal models typically use.

It is here that graph-based systems model the data points and connect them across modalities as optimized graph edges, helping build learning systems that can perform a wide range of tasks.

Applications Across Domains

The diverse capabilities of multimodal models make them a great fit for a variety of applications that require high accuracy and precision in results.

1. Natural Language Processing and Vision Integration

The advancements in artificial intelligence have led to a continuous improvement in the way that computers interact with humans. An excellent example is ChatGPT, which leverages multimodal models to hold realistic conversations.

The model combines the prowess of natural language processing and computer vision, enabling it to generate reliable information with deep context and relevance for a query.

These models today play a crucial role in interpreting text based content and augment the interpretations with integrated visual information, sometimes aural data as well, significantly improving the contextual relevance of the output.

Further applications include Google Gemini, which works on the same principles. Gemini learns continuously through a collection of cues that are provided to it, drawing inferences and putting the gleaned data together to build context in real-time.

Google explains how Gemini was able to identify patterns and secret messages through user inputs that consisted primarily of textual and visual information for the AI engine. It was even able to tell a magic trick apart!

2. Cross-Modal Retrieval

Cross-modal retrieval is a key capability augmentation that multimodal models bring to the table. It is especially useful where the user initiates a query with one type of data, with the aim to retrieve relevant data that is of a different modality.

Given that multimodal models are trained using data of different types, they are able to form relationships and dependencies between data points, allowing them to perform cross-modal data retrieval.

Application of this capability is particularly useful in data classification and management operations where text-based tags or labels can be used to retrieve information of another type.

Users can input tags like “plant with purple flowers” as a query in the multimodal AI model. It will then work on cross-modal data retrieval to generate or fetch images that match the tags in the sentence.

It is also extremely helpful in organizational operations where data is required to be presented at the fingertips for quick decision-making.

3. Human-Computer Interaction

Multimodal models can significantly streamline human-computer interactions by smoothening the creases in communication. Using a combination of natural language processing, cross-modal retrieval, and computer vision, it is possible to develop applications that improve the conversational quality and results between a computer and a human.

A good example is that of Iron Man’s Jarvis, an AI capable of holding normal conversations and generating relevant and accurate results to the queries asked.

The inputs and outputs in HCI using multimodal models are flexible, like handwriting, speech, and even gestures. HCI systems are capable of leveraging more complex methods of communication, like pattern recognition and classification, facial expression, and even touch.

The applications of HCI systems are of immense importance in creating assistive technologies that enable inclusive participation of people with disabilities.

Not requiring any specific type of input, HCI can simplify computer usage for challenged individuals who can combine a variety of input forms to achieve the end goal with a computer.


Technologies are advanced through encountering challenges and innovating for improvement. Multimodal models, too, pose some challenges with adoption:

1. Technical Challenges

Alignment is one of the major technical challenges that multimodal models represent.

Multimodal data consists of datasets that have some correlation between them. It is the job of the model to piece two and two together and achieve an alignment between data points that represent the same thing.

In order to align different modalities, the model needs to map the similarities between them and manage their dependencies. This problem is amplified by a lack of annotated datasets and multiple correct alignments.

For example, the existence of text-based steps to repair a laptop, accompanied by a video for the same, requires accurate mapping to align the right steps with the right timestamp in the video.

One potential solution to achieve better relationships between text and associated media is for data scientists to experiment with OCR engines to generate text instead of traditional vision-language pretraining.

2. Ethical Considerations

Multimodal systems comprise natural language processing and computer vision components. Both are trained simultaneously on datasets to achieve combined embedding spaces.

Given that these datasets are not free of biases, it is inevitable that, as an undesirable but technical necessity, the multimodal model may pick up and build on the same biases.

This is typically the case where the multimodal datasets lack diversity, which is necessary to impart multifaceted perspectives to the model to operate with neutrality.

One potential solution to preventing multimodal models from learning existing dataset biases is to provide them with larger and more comprehensive training data. The diversity in data helps to introduce multiple perspectives and observations to the entire dataset, enabling the model to develop neutrality in reasoning.

Future Trends

The capabilities and prospective applications of multimodal learning are expanding rapidly. The continual advancements in various applications of AI are helping leverage multimodal models across industries in the form of:

1. Internet of Things

IoT already exists across a variety of applications around the world – in homes, offices, manufacturing facilities, and even security. The information these systems collect is multimodal, providing an opportunity for multimodal systems to analyze complex situations and trigger alerts for further action or bring humans into the loop.

2. Analytics of Big Data

Understanding that multimodal models require large volumes of diversified data to learn properly from, there is immense potential to apply these algorithms to the big data that enterprises deal with.

Even on a daily basis, the nature of Big Data is so varied and diverse that it creates a fertile soil to generate invaluable business insights.

It can be expected that the near future will also see more effective training methods for multimodal models. These methods can handle the common training challenges using methods like early fusion, late and hybrid fusion to streamline data integration problems.

Case Studies and Success Stories

Multimodal AI models have promising capabilities that are being leveraged across a variety of industries for streamlining workflows and saving costs. Let’s take a look at two case studies that highlight the successful use of multimodal models.

Predicting Outcomes of Clinical Trials Using Multimodal Models

Drug development is a costly affair, chiefly because clinical trials frequently lead to high failure rates, which ultimately add to development costs. It is here that the application of multimodal AI models was tested by Insilico Medicine to predict clinical trial outcomes to help save billions of dollars.

Additionally, these predictions were also targeted at prioritizing the more beneficial drug trial programs with robust results.

The process involved the examination of multimodal data, including transcriptomic data, text-mined, target, indication representations, and clinical trial protocols.

The model was able to identify 19 trials with potential for success in H2 2023. You can read more about this case study here.

Leveraging Multimodal Models for Document Classification

Document classification is a universal challenge that all types of automated systems struggle to manage. In this independent case study, multimodal models have been applied to the task to compare the performance against an unimodal model.

The study used training data from a variety of document types, like resumes, emails, forms, letters, memos, news, notes, reports, etc., from open-source networks and 3308 image-text pairs.

Two tests were conducted: accuracy of a model trained on unimodal data versus multimodal data. The study found that the unimodal model had an accuracy of 86.2%, while the multimodal data training resulted in an accuracy of 94.5%, an increase of 8%.

You can read more about this case study here.

Closing Thoughts

The tidal wave of advancements in AI and associated models requires businesses to continuously keep up with the innovations. Multimodal models have tremendous potential in not just providing supportive output, but creative and innovative input to the entirety of business operations as well.

Being able to leverage the immense potential vested in multimodal models requires establishing a robust wireframe of AI infrastructure.

It allows enterprises to build reliable GenAI and intelligence apps that are dynamic and respond to business requirements. MarkovML's AI platform is built for efficiency, performance, and reliability - the three qualities that businesses mandate for building trustworthy AI solutions.

MarkovML enables enterprises to build AI solutions for complex operations like data intelligence, ML workflows, and more using no-code methodologies. The simplification of the entire process helps you get ahead in the game by quickly mobilizing your productions. 

To understand MarkovML's capabilities in-depth, visit the website.

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