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January 02, 2024

Multimodal AI Models: Understanding Their Complexity

Author:




Edwin Lisowski

CSO & Co-Founder


Reading time:




19 minutes


The past decade has seen monumental advancements in the field of AI. While traditional AI models and technologies focus primarily on data analysis, current technologies like deep learning, machine learning, NLP, and generative AI take a more expansive approach to how they process data.

To this effect, developers and data scientists have come up with several technologies to enhance data processing on an expansive scale. One such technology is Multimodal AI. This revolutionary technology can integrate information from diverse sources to give a better understanding of the data at hand, thus enabling organizations to unlock new insights and support a broader range of applications.

This article will explore the fundamentals of multimodal AI models, exploring everything from what they are, how they work, and the various benefits and challenges they present.

What is multimodal AI?

To understand what multimodal AI is, you first need to understand the concept of modality. Modality, in its simplest form, refers to how something happens or is experienced. From this standpoint, anything that involves multiple modalities can be described as multimodal.

ContextClue baner

 

Multimodal artificial intelligence is a subset of artificial intelligence that focuses on leveraging multiple modalities to build more accurate and comprehensive AI models. Multimodal models can effectively combine information from different sources, such as images, text, audio, and video, to create a more accurate and comprehensive understanding of the underlying data. [1]

This unique approach to data processing comes in handy in a wide range of applications, including autonomous cars, speech recognition, and emotional recognition. Multimodal models can also perform various other tasks, including text-to-image generation, visual question-answering, and robotics navigation.

Multimodal VS. Unimodal AI models

Despite performing relatively similar functions, multimodal and unimodal AI models take distinctive approaches to developing AI systems. For instance, unimodal models focus on training AI models to execute a specific task using one source of data. Multimodal models, on the other hand, combine data from different sources to effectively analyze a given problem.

Unimodal AI models vs. Multimodal

Multimodal and unimodal AI models also differ in terms of:

Scope of Data

The biggest difference between unimodal and multimodal AI models lies in their scope of data. The former, unimodal models, are built to analyze and process one type of data. Conversely, multimodal models combine multiple modalities and integrate them into a unified system capable of handling multiple types of data, including text, video inputs, and audio.

Context

The value of how accurate a model comes down to its ability to understand context. In this regard, unimodal models face a tremendous disadvantage due to their data processing limits.

Multimodal models, on the other hand, have diverse infrastructures made up of different modalities. This enables them to analyze a given problem more comprehensively, thus getting more context.

Complexity

As single-modality models, unimodal AI models are significantly less complex compared to their multimodal counterparts, which have intricate structures made of multiple modalities and other systems.

Performance

Both unimodal and multimodal models can perform pretty well in their designated tasks. However, unimodal models incur significant challenges when dealing with a problem that requires a broader context.

Multimodal AI models, on the other hand, can handle context-intensive tasks seamlessly. This is due to their ability to integrate and analyze multiple modalities to get more context.

Data Requirements

The domain expertise required from unimodal models means that they require a tremendous amount of a single data type to train.

Contrarily, multimodal models integrate multiple sources of data, which means that they don’t really require that much data to train – they can simply leverage the domain-specific expertise provided by the multiple modalities in the system.

Multimodal AI Models: Comparing Combining Models vs. Multimodal Learning

Multimodal AI takes two different approaches: combining models and multimodal learning. While both concepts might sound similar, they have differences that make them distinctively unique technologies.

Let’s have a look at both concepts to get a clear understanding.

Combining models

As the name suggests, combining models is a concept in machine learning that involves combining multiple models to improve the primary model’s performance. The notion behind this concept is pretty simple; if all models have unique strengths and weaknesses, combining multiple models may help overcome the models’ weaknesses, resulting in more robust and accurate predictions. [2]

Some of the most common techniques used in combining models include:

  • Ensemble models
  • Stacking
  • Bagging

Ensemble Models

The ensemble model technique involves combining the outputs of several base models to create a better overall model. Take random forests, for instance. This ensemble model is basically a decision-tree algorithm that combines different decision trees to enhance the model’s accuracy. [3]

Typically, each decision tree is trained on a different subset of the data, then the algorithm averages the predictions of all trees to formulate a more accurate prediction.

Stacking

Staking works much like the ensemble models technique but with one key difference. Instead of averaging the predictions of multiple models, stacking involves using the outputs of multiple models as the input for a single primary model.

One of the most notable applications of stacking is in NLP, where it can be used for sentiment analysis. [4]

The Stanford Sentiment Treebank dataset, for instance, contains movie reviews with varying sentiment labels ranging from negative to very positive. Developers can use the data set to train several models, including Random Forests, Naive Bayes, and Support Vector Machines (SVM), to predict the reviews’ sentiment.

To make the predictions more accurate, developers can combine the predictions of the various models using meta-models like Neural Networks and Logistic Regression, which are typically trained on the outputs of the base models to make a final prediction.

Bagging

Bagging typically involves training several base models on different subsets of data and averaging the predictions to make a final prediction.

There are several techniques involved in bagging, the most notable of which is the bootstrap aggregating method. In the bootstrap aggregating method, data scientists train multiple models on different subsets of the training data and make a final prediction by averaging the predictions of all the models. [5]

Multimodal learning

Multimodal learning is a subset of machine learning that trains AI models to process and analyze different types of data, including text, images, audio, and video. By combining these sources of data (modalities), multimodal learning can enable a model to get a more comprehensive understanding of its environment and context since certain cues only exist on specific types of data.

Think of it this way: it’s not enough to just look at the world. To understand it, you also need to hear sounds and touch objects. This gives you a wholesome view of the world around you.

These models rely on deep neural networks to process different types of data individually before integrating them into a unified representation.

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Complexity of multimodal models in AI

Multimodal AI is a complex field, to say the least. The various modalities that make up the system can range from raw, sensor-detected data, such as images and recordings, to abstract concepts like object analysis and sentiment intensity.

However, all this has to come from somewhere. That’s where the principles of multimodal AI come into play. According to a recent survey by Carnegie Mellon researchers, the key principles of modality include:[6]

  • Heterogeneity
  • Connections
  • Interactions

Heterogeneity

Modalities are heterogeneous. They often exhibit diverse structures, qualities, and representations. Take video and audio recordings of the same event, for instance. Despite representing the same sequence of events, they may convey different information, which instinctively requires unique processing and analysis.

Similarly, there are six dimensions of heterogeneity. They include:

  • Element representation
  • Structure
  • Distribution
  • Noise
  • Information
  • Relevance

These dimensions measure differences in sample space, likelihood of elements, frequencies, information content, underlying structure, and tax/context relevance.

Connections

One of the most common underlying characteristics of modalities is they often share complementary information and, hence, can work together to create new insights. Researchers in the field typically study these connections through statistical analysis of associations and semantic correspondence.

The two techniques are further characterized by bottom-up and top-down reasoning. Bottom-up reasoning involves statistical association (whereby the value of one variable relates to the value of another variable) and statistical dependence (where it is vital to understand the exact type of dependency between two elements.

The latter, top-down reasoning, involves semantic correspondence and semantic relations. Semantic correspondence identifies similar elements between modalities. Conversely, semantic relations include an attribute that describes the nature of the relationship between two modality elements. The various attributes include logical, semantic, functional, and causal.

Interactions

Multimodal models interact differently when integrated into a task. The way in which they integrate can take many forms, such as how a speech recording and an image can be combined to recognize a person or an image.

There are three major dimensions of interactions: interaction mechanics, interaction information, and interaction response.

Interaction mechanics describes the various operators involved when integrating elements in a modality for task inference. Interactive information, on the other hand, investigates the type of information involved in a specific interaction. Finally, interaction response describes the study of how inferred responses change in the presence of multimodal modalities.

How do multimodal models work?

Multimodal AI models work by combining multiple sources of data from different modalities, including text, video, and audio. The systems’ working mechanism starts with training individual neural networks on a specific type of data. The training process typically employs recurrent neural networks on text-based modalities and convolutional neural networks on image modalities.

How do multimodal models work?

To provide an output, these systems first need to capture the crucial features and characteristics of the input data. To achieve this, they rely on three primary components, with the process starting with unimodal encoders.

Unimodal encoders process each individual modality’s data separately. i.e., a text encoder processes text, while an image encoder processes images.

After passing through the unimodal encoders, the data makes its way to the fusion network. The primary role of the fusion network is to combine the features and characteristics of the data extracted by the unimodal encoders from various modalities into a single, unified representation. This process utilizes various techniques, including attention mechanisms, concatenation, and cross-model interactions.

The classifier makes up the final component of the model’s architecture. The classifier basically makes accurate predictions based on the input or classifies the fused representations into a specific output category.

One of the biggest benefits of multimodal models is their modularity. Multimodal models’ modularity allows for better flexibility and adaptability in combining different modalities and dealing with new inputs and tasks. The models’ system of combining information from different modalities allows multimodal models to offer better performance and more accurate predictions compared to their unimodal counterparts.

Discover more about Multimodal Models: Integrating Text, Image, and Sound in AI

How do multimodal models work for different types of inputs?

Here’s how the multimodal AI architecture works for different types of inputs. To make it easier to understand, we’ve also included real-life examples.

Text-to-image generation and image description generation

GLIDE, CLIP, and DALL-E are some of the most revolutionary models of the decade. These incredible models can generate images from text and help describe images.

OpenAI’s CLIP leverages separate text and image encoders. These encoders are trained on massive datasets to predict the specific images in a dataset associated with certain descriptions. The model also leverages multimodal neurons that are trained to activate whenever the model is exposed to both an image and the matching text description, indicating a representation of a fused multimodal system.[7]

Conversely, DALL-E, a popular variant of the GPT-3 models with up to 13 billion parameters, generates a series of images that match the input prompt. The model then utilizes CLIP to rank the images, thus producing accurate, detailed images. [8]

Like DALL-E, GLIDE also utilizes CLIP to rank generated images. However, unlike DALL-E, GLIDE utilizes a diffusion model to generate more accurate and realistic images.

Visual question answering

In VAQ, a model is required to answer a question correctly based on a presented image. Microsoft Research is one of the leading companies in developing creative, innovative approaches for visual question-answering.

Take METRE, for instance. The team’s general framework application utilizes multiple sub-architectures for vision encoders, decoder modulus, text encoders, and multimodal fusion modules.

The Unified Vision-Language Pretrained Model (VLMo) also offers an interesting approach to VQA. The model utilizes various encoders including a dual encoder, fusion encoder, and a modular transformer network for learning purposes. The model’s network is made of multiple self-attention layers and blocks with modality-specific experts, thus providing unmatched flexibility when fine-tuning the model.

Image-to-text search and text-to-image

Multimodal learning is poised to revolutionize web search. Take the WebQA dataset, for instance. The dataset was created by data scientists and developers from Carnegie Mellon University and Microsoft. When utilized correctly, it enables web-search models to accurately identify text and image-based sources that can help in answering a query.

That said, the model requires multiple sources to provide accurate predictions. The model then has to ‘reason’ with the multiple sources to provide an answer to the query in natural language.

Similarly, Google’s ALIGN (Large-scale ImaGe and Noisy-Text Embedding model) utilizes alt-text data from internet images to train text (BERT-Large) and distinct visual (EfficientNet-L2) encoders.

The multimodal architecture then fuses the outputs of these encoders using contrastive learning, which results in powerful models with multimodal representation capable of powering web searches across multiple modalities without further training or fine-tuning.

Video-language modeling

The resource-exhaustive nature of video-language modeling tasks poses significant challenges for AI systems. To tackle the issue and inch closer to AI natural intelligence, experts have developed multimodal models with video-related capabilities.

Take Project Florence-VL by Microsoft, for instance. Their project, ClipBERT utilizes a combination of transformer models and Convolutional Neural Networks (CNN) operating on thinly sampled frames.

Other iterations of ClipBERT, like SwinBERT and VIOLET, utilize Sparse Attention and Visual-token Modeling to achieve a state-of-the-art status in tasks related to video question answering, captioning and retrieval.

ClipBERT, SwinBERT, and VIOLET all share a similar transformer-based architecture, which is typically combined with parallel learning modules that enable them to extract video data from multiple modalities and integrate them into a unified multimodal representation.

Benefits of multimodal AI models

Some of the most notable benefits of utilizing multimodal AI models include:

Contextual understanding

Multimodal AI systems can understand the meaning of a phrase or sentence by analyzing the surrounding concepts and words.

This is crucial in natural language processing tasks, where it is vital for a model to understand the concept of a sentence and generate an appropriate response. When combined with multimodal AI, NLP models can combine linguistic and visual information to attain a more well-rounded understanding of the context.

Multimodal models can consider both the textual and visual cues in a particular context by combining multiple modalities. For instance, image captioning models can interpret the visual information contained in an image and merge it with the relevant linguistic information on the caption.

Similarly, video captioning multimodal models are able to understand both the visual information in a video and the temporal relationship between the sounds, events, and dialogue in the video.

The contextual understanding of multimodal models also comes in handy in the development of natural language dialogue systems like chatbots. By using linguistic and visual cues, multimodal models are better able to generate more human-like responses in a conversation.

Improved accuracy

By integrating multiple modalities such as text, images, and videos, multimodal models are better able to provide greater accuracy. Multimodal AI models can capture a more nuanced, comprehensive understanding of the input data, which results in better performance and accurate predictions across a wide range of tasks.

Multiple modalities allow multimodal AI models to develop more descriptive and precise captions in tasks like image captioning. They also enhance other operations like natural language processing tasks by incorporating facial and speech recognition to get more accurate insights into the emotional state of the speaker.

Multimodal models are also more resistant to incomplete, noisy data since they can fill in the missing gaps or fix errors by utilizing information from multiple modalities. For instance, a model that incorporates lip movements into its speech recognition tasks can enhance its accuracy in noisy environments, thus enabling clarity, even when the audio quality is unclear.

Natural interaction

Multimodal models can facilitate natural interactions between users and machines. Previous AI models only had one mode of input, such as speech or text, which limited their interaction capabilities. Multimodal models, on the other hand, can combine multiple modes of input, including text, speech, and visual cues, to understand a user’s needs and intentions more comprehensively.

For instance, when incorporated into a virtual assistant, a multimodal AI system could use text and speech recognition to understand a user’s commands. It can also incorporate other relevant information, such as the user’s gestures and facial expressions, to determine their level of engagement. Ultimately, this can help create a more tailored, engaging experience.

Multimodal AI systems can also facilitate more effective NLP, allowing humans to interact with machines conversationally. For instance, a chatbot can use natural language understanding (NLU) to interpret a user’s message and then combine it with information from the user’s visual cues or images to get a comprehensive understanding of the user’s tone and emotions. This can enable the chatbot to provide more nuanced and effective responses.

Improved capabilities

Multimodal models have the ability to significantly improve the overall capabilities of an AI system, especially in cases where the model can leverage information from multiple modalities, including image, text, and audio, to understand the context. Ultimately, this helps AI systems to perform more diverse tasks, with greater performance, accuracy and effectiveness.

For example, a multimodal model that combines facial and speech recognition could serve as an effective system for identifying individuals. Similarly, analyzing both audio and visual cues would assist the model in differentiating objects and individuals with similar voices and appearances. Also, a deeper analysis of contextual information like the behavior or environment would provide a more comprehensive understanding of the situation, which would ultimately lead to more informed decisions.

There’s also the issue of humans interacting with technology. Multimodal AI systems can facilitate seamless, natural, and intuitive interactions with devices, thus making it much easier for people to interact with AI systems.

Think of it this way: By combining different modalities like gesture and voice recognition, a multimodal-powered AI system can comprehend and respond to more complex queries and commands, leading to improved user satisfaction and effective utilization of technology.

Real-life use cases of multimodal AI

Several companies in different sectors have seen the technology’s potential and incorporated it into their digital transformation agendas. Here’s a sneak peek into some of the most notable use cases of multimodal AI.

Healthcare and pharma

The healthcare sector is always quick to utilize any technology that can improve its service delivery. This notion also applies to multimodal AI, where hospitals can benefit from more accurate and reliable diagnoses, better treatment outcomes, and personalized treatment plans.

Their ability to analyze data from multiple modalities such as symptoms, background, image data and patient history enables multimodal AI models to help healthcare professionals make informed diagnostic decisions quicker and more efficiently.

In the healthcare sector, multimodal AI can help medical professionals diagnose complex conditions by analyzing medical images like MRIs, X-rays, and CT scans. When combined with other data like clinical histories and patient data, these modalities provide a more thorough understanding of the condition, thus helping medical professionals make accurate diagnoses.

Similarly, the pharmaceutical sector can benefit from multimodal AI by leveraging the system to predict the suitability of potential drug candidates and identify new drug targets.

By analyzing data from disparate sources, including genetic data, electronic health records, and clinical trials, multimodal AI models can identify various patterns and relationships in the data that may not be apparent to a human researcher. This can help pharmaceutical companies identify potential drug candidates and bring drugs into the market quickly.

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Automotive industry

The automotive sector is one of the earliest adopters of multimodal AI technology. Companies in the sector leverage the technology to enhance convenience, safety, and overall driving experience. In the past few years, the automotive sector has seen significant strides in integrating multimodal AI systems into HMI (human-machine interface) assistants, driver assistance systems, and driver monitoring systems.

Modern vehicles’ HMI (human-machine interface) technology has seen a monumental boost from multimodal AI by enabling voice and gesture recognition, which facilitates easier interactions between drivers and their vehicles.

Additionally, driver monitoring systems powered by multimodal AI can effectively detect driver drowsiness, fatigue, and inattention through various modalities, including eye-tracking, facial recognition, and steering wheel movements.

Wrapping up

Multimodal AI has applications in virtually every industry. Its multifaceted approach to data processing allows more accurate predictions, which could significantly improve business processes and customer satisfaction across numerous sectors.

While it may be one of the most complex AI model technologies on the market, multimodal AI is poised to change how we live, work, and do business. And, as the technology continues to evolve, we may see further alliterations that can do things that were previously deemed unachievable with AI systems.

References

[1] Techtarget.com. Multimodal AI. URL: https://www.techtarget.com/searchenterpriseai/definition/multimodal-AI, Accessed on December 20, 2023
[2] CS.toronto.edu, Ensemble. URL:  https://www.cs.toronto.edu/~urtasun/courses/CSC2515/08ensemble-2515.pdf Accessed on December 21, 2023
[3] Towardsdatascience.com. URL: https://towardsdatascience.com/random-forests-an-ensemble-of-decision-trees-37a003084c6c
[4] Towardsdatascience.com. Stacking ML Models for Speech Sentiment Analysis. URL: https://bit.ly/3tMVOeO.  Accessed on December 21, 2023
[5] Simplilearn.com. Bagging in ML. URL: https://bit.ly/3RLLvjr. Accessed on December 21, 2023
[6] Arxiv.org. Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions. URL: https://arxiv.org/abs/2209.03430, Accessed on December 21, 2023
[7] Cv-tricks.com. Understanding Clip By Openai. URL: https://bit.ly/3S2A73X. Accessed on December 21, 2023
[8] Cointelegraph.com. What is DALL-E and how Does it Work. URL: https://cointelegraph.com/news/what-is-dall-e-and-how-does-it-work, Accessed on December 21, 2023
[9] Microsoft.com. Project Florence-VL. URL: https://www.microsoft.com/en-us/research/project/project-florence-vl. Accessed on December 21, 2023



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