What Is Multimodal Learning In Machine Learning?

Multimodal learning in machine learning is the process of training AI models to interpret and relate different types of data, like images, video, audio, and text, which is crucial for AI to match human intelligence, and at LEARNS.EDU.VN, we offer extensive resources to help you master this fascinating field. Dive into the world of data fusion, cross-modal analysis, and multimodal data processing to unlock new possibilities in AI, where you’ll gain skills that will help you become a specialist in machine learning and artificial intelligence.

1. Defining Multimodal Learning

Multimodal machine learning is the study of algorithms that learn and improve performance by using multimodal datasets. It focuses on enabling AI models to process and understand relationships between different data types, known as modalities. Typically, these modalities include visual (images, videos), textual, and auditory (voice, sounds, music) data. By integrating diverse modalities, machine learning models achieve a more comprehensive understanding of their environment. This is crucial because certain cues are exclusive to specific modalities.

For example, consider emotion recognition. It involves more than just analyzing facial expressions (visual modality). The tone and pitch of a person’s voice (audio modality) provide substantial information about their emotional state, which might not be apparent from their facial expressions, even though they are often synchronized.

Unimodal models, which process only a single modality, have been extensively researched and have greatly advanced fields such as computer vision and natural language processing. However, the limitations of unimodal deep learning underscore the need for multimodal models. The image below illustrates how unimodal models can struggle with tasks such as recognizing sarcasm or hate speech. This example is taken from META’s multimodal dataset, “Hateful Memes.”

Combining image and text to create a sarcastic meme. Unimodal models are unable to perceive such kind of sarcasm since each individual modality contains just half the information. In contrast, a multimodal model that processes both text and images can relate the two and discover the deeper meaning. source

While multimodal models often use deep neural networks, earlier research also incorporated other machine learning models like Hidden Markov Models (HMM) or Restricted Boltzmann Machines (RBM). In multimodal deep learning, the most common modalities are visual (images, videos), textual, and auditory (voice, sounds, music). However, other modalities can include 3D visual data, depth sensor data, and LiDAR data (commonly used in self-driving cars). In healthcare, imaging modalities include computed tomography (CT) scans and X-ray images, while non-image modalities include electroencephalogram (EEG) data. Sensor data, such as thermal data or data from eye-tracking devices, can also be included.

Any combination of the above unimodal data results in a multimodal dataset. For example:

• Combining video, LiDAR, and depth data creates an excellent dataset for self-driving car applications.
• Combining EEG and eye-tracking device data creates a multimodal dataset that connects eye movements with brain activity.

The most popular combinations include:

• Image + Text
• Image + Audio
• Image + Text + Audio
• Text + Audio

LEARNS.EDU.VN offers courses and resources to help you understand and work with these diverse modalities effectively.

2. Core Challenges in Multimodal Learning

Multimodal deep learning aims to address five core challenges, which are active areas of research. Addressing or improving these challenges will drive advancements in multimodal AI research and practice.

2.1. Representation

Multimodal representation involves encoding data from multiple modalities into a vector or tensor format. Effective representations that capture the semantic information of raw data are crucial for the success of machine learning models. However, extracting features from heterogeneous data in a way that leverages the synergies between them is difficult. It is essential to fully exploit the complementarity of different modalities while minimizing attention to redundant information.

Multimodal representations fall into two main categories:

1. Joint Representation: In this approach, each individual modality is encoded and then placed into a shared high-dimensional space. This method is straightforward and works well when modalities are similar in nature.

2. Coordinated Representation: Here, each individual modality is encoded independently, but their representations are coordinated by imposing a restriction. For example, their linear projections should be maximally correlated.

   $$(u,v) = argmax_{u,v}(u^TX,v^TY)$$

   Where (X, Y) denote input modalities, ((u^T, v^T)) denote matrices that transfer input modalities to some representation space, and ((u^, v^)) denote the desired representation matrices that transfer inputs to a mutual representation space after the restriction has been imposed.

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2.2. Fusion

Fusion is the process of combining information from two or more modalities to perform a prediction task. Effectively fusing multiple modalities, such as video, speech, and text, is challenging due to the heterogeneous nature of multimodal data.

Fusing heterogeneous information is central to multimodal research but presents a significant set of challenges. Practical challenges involve solving problems such as different formats, lengths, and non-synchronized data. Theoretical challenges involve finding the most optimal fusion technique. Options include simple operations like concatenation or weighted sum, and more sophisticated attention mechanisms such as transformer networks or attention-based recurrent neural networks (RNNs).

Moreover, one must choose between early or late fusion. In early fusion, features are integrated immediately after feature extraction using some of the fusion mechanisms mentioned above. In late fusion, integration is performed only after each unimodal network outputs a prediction (classification, regression). Voting schemes, weighted averages, and other techniques are typically used in late fusion. Hybrid fusion techniques have also been proposed, combining outputs from early fusion and unimodal predictors.

LEARNS.EDU.VN offers detailed guides on different fusion techniques, helping you choose the best approach for your specific needs.

2.3. Alignment

Alignment refers to identifying direct relationships between different modalities. Current research in multimodal learning aims to create modality-invariant representations. This means that when different modalities refer to a similar semantic concept, their representations should be similar or close together in a latent space. For example, the sentence “she dived into the pool,” an image of a pool, and the audio signal of a splash sound should be close together in a manifold of the representation space.

2.4. Translation

Translation is the act of mapping one modality to another. The main idea is how one modality (e.g., textual) can be translated to another (e.g., visual) while retaining the semantic meaning. Translations are open-ended, subjective, and lack a perfect answer, adding complexity to the task.

Current research in multimodal learning focuses on constructing generative models that translate between different modalities. The recent DALL-E and other text-to-image models are excellent examples of generative models that translate text modalities to visual modalities.

2.5. Co-Learning

Multimodal co-learning aims to transfer information learned through one or more modalities to tasks involving another. Co-learning is particularly important in cases of low-resource target tasks, fully or partly missing modalities, or noisy modalities.

Translation, as explained above, can be used as a method of co-learning to transfer knowledge from one modality to another. Neuroscience suggests that humans use methods of co-learning through translation as well. People with aphantasia, the inability to create mental images, perform worse on memory tests. Conversely, people who create such mappings, textual/auditory to visual, perform better on memory tests. This suggests that converting representations between different modalities is an important aspect of human cognition and memory.

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3. How Multimodal Learning Works

Multimodal neural networks are usually a combination of multiple unimodal neural networks. For example, an audiovisual model might consist of two unimodal networks, one for visual data and one for audio data. These unimodal neural networks typically process their inputs separately, a process called encoding. After unimodal encoding, the information extracted from each model must be fused together. Multiple fusion techniques have been proposed, ranging from simple concatenation to attention mechanisms. The process of multimodal data fusion is a critical success factor. After fusion, a final “decision” network accepts the fused encoded information and is trained on the end task.

In summary, multimodal architectures usually consist of three parts:

1. Unimodal encoders that encode individual modalities, typically one for each input modality.
2. A fusion network that combines the features extracted from each input modality during the encoding phase.
3. A classifier that accepts the fused data and makes predictions.

These components are referred to as the encoding module (DL Module), fusion module, and classification module.

Workflow of a typical multimodal. Three unimodal neural networks encode the different input modalities independently. After feature extraction, fusion modules combine the different modalities (optionally in pairs), and finally, the fused features are inserted into a classification network.

3.1. Encoding

During encoding, the goal is to create meaningful representations. Each individual modality is typically handled by a different monomodal encoder. However, the inputs are often in the form of embeddings instead of their raw form. For example, word2vec embeddings may be used for text, and COVAREP embeddings for audio. Multimodal embeddings such as data2vec, which translate video, text, and audio data into embeddings in a high-dimensional space, are a recent practice and have outperformed other embeddings, achieving state-of-the-art performance in many tasks.

Deciding whether to use joint representations or coordinated representations (explained in the representation challenge) is an important decision. A joint representation method typically works well when modalities are similar in nature and is the most commonly used.

In practice, when designing multimodal networks, encoders are chosen based on what works well in each area since more emphasis is given to designing the fusion method. Many research papers use ResNets for visual modalities and RoBERTA for text.

3.2. Fusion

The fusion module is responsible for combining each individual modality after feature extraction is completed. The method or architecture used for fusion is likely the most important factor for success.

The simplest method is to use simple operations such as concatenating or summing the different unimodal representations. However, more sophisticated and successful methods have been researched and implemented. For example, the cross-attention layer mechanism is one of the more recent and successful fusion methods. It has been used to capture cross-modal interactions and fuse modalities in a more meaningful way. The equation below describes the cross-attention mechanism and assumes basic familiarity with self-attention:

$$alpha_{kl} = s(frac{K_lQ_k}{sqrt{d}})V_l$$

Where (alpha{kl}) denotes the attention score vector, (s(.)) denotes the softmax function, and (K), (Q), and (V) are the Key, Query, and Value matrices of the attention mechanism, respectively. For symmetry, (alpha{lk}) is also computed, and the two may be summed up to create an attention vector that maps the synergy between the two modalities ((k,l)) involved. The difference between (alpha{kl}) and (alpha{lk}) is that in the former, (modality_k) is used as the query, while in the latter, (modality_l) is used instead, and (modality_k) takes the role of key and value.

In the case of three or more modalities, multiple cross-attention mechanisms may be used so that every different combination is calculated. For example, with vision (V), text (T), and audio (A) modalities, the combinations VT, VA, TA, and AVT are created to capture all possible cross-modal interactions.

Even after using an attention mechanism, a concatenation of the cross-modal vectors is often performed to produce the fused vector (F). Sum(.), max(.), and even pooling operations may also be used instead.

3.3. Classification

After fusion, vector (F) is fed into a classification model, typically a neural network with one or two hidden layers. The input vector (F) encodes complementary information from multiple modalities, providing a richer representation compared to the individual modalities V, A, and T, thereby increasing the predictive power of the classifier.

Mathematically, the aim of a unimodal model is to minimize the loss:

$$L(C(phi_m(X)),y)$$

where (phi_m) is an encoding function, typically a deep neural network, and (C(.)) is a classifier, typically one or more dense layers.

In contrast, the aim of multimodal learning is to minimize the loss:

$$L{multi}(C(phi{m1} oplus phi{m2} oplus cdot cdot cdot oplus phi{m_k}),y)$$

where (oplus) denotes a fusion operation (e.g., concatenation), and (phi_{m_i}) denotes the encoding function of a single modality.

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4. Applications of Multimodal Deep Learning

Here are some examples of multimodal deep learning applications in computer vision:

4.1. Image Captioning

Image captioning is the task of generating short text descriptions for a given image. It’s a multimodal task involving multimodal datasets consisting of images and short text descriptions. It addresses the translation challenge by translating visual representations into textual ones. The task can also be extended to video captioning, where text coherently describes short videos.

For a model to translate visual modalities into text, it must capture the semantics of a picture. It needs to detect the key objects, key actions, and key characteristics of objects. For example, “A horse (key object) carrying (key action) a large load (key characteristic) of hay (key object) and two people (key object) sitting on it.” Moreover, it needs to reason about the relationships between objects in an image, e.g., “Bunk bed with a narrow shelf sitting underneath it (spatial relationship).”

The task of multimodal translation is open-ended and subjective. Hence, captions like “Two men are riding a horse carriage full of hay” and “Two men transfer hay with a horse carriage” are also valid.

Image captioning models can provide text alternatives to images, assisting blind and visually impaired users.

Examples of image captioning, images on top with short text explanations below source

4.2. Image Retrieval

Image retrieval involves finding images within a large database that are relevant to a retrieval key. This task is also known as Content-Based Image Retrieval (CBIR) and Content-Based Visual Information Retrieval (CBVIR).

This can be done through a traditional tag-matching algorithm, but deep learning multimodal models offer a broader solution with more capabilities, partially eliminating the need for tags. Image retrieval can be extended to video retrieval. The retrieval key can be a text caption, an audio sound, or even another image, but text descriptions are the most common.

Several cross-modal image retrieval tasks have been developed, including:

• Text-to-image retrieval: Retrieving images related to text explanations.
• Composing text and image: Using a query image and text that describes desired modifications.
• Cross-view image retrieval.
• Sketch-to-image retrieval: Using a human-made pencil sketch to retrieve relevant images.

When you make a search query on your browser and the search engine provides an “images” section showing images related to your query, it’s a real-world example of image retrieval.

An example of multimodal image retrieval, using composing text + image method. The fetched images are fetched through a database if they meet the criteria of the query image and the text’s description source

4.3. Text-to-Image Generation

Text-to-image generation is currently one of the most popular multimodal learning applications, directly addressing the translation challenge. Models like Open-AI’s DALL-E and Google’s Imagen have been making headlines.

These models do the inverse of image captioning. Given short text descriptions as a prompt, a text-to-image model creates a novel image that accurately reflects the text’s semantic meaning. Recently, text-to-video models also made their debut.

These models can aid photoshopping and graphics design while also providing inspiration for digital art.

Example of text-to-image generation. The text on the bottom acts as a prompt, and the model creates the novel image depicted on top source

4.4. Visual Question Answering (VQA)

Visual Question Answering is another multimodal task that combines visual modalities (image, video) with text modality. During VQA, the user asks a question about an image or video, and the model must answer based on what is happening in the image. A strong visual understanding of a scene, along with common-sense knowledge, is required to tackle this problem successfully. Simple examples of closed-form VQA include “How many people are in the picture?” and “Where is the child sitting?” However, VQA can expand to free-form, open-ended questions that require a more complex thought process, as illustrated in the image below.

Visual question answering is a multimodal application that incorporates both translation and alignment challenges.

These models can help blind and visually impaired users or provide advanced visual content retrieval.

Examples of open-ended, free-form questions for VQA tasks. Answering requires a complex thought process, precise decoding, and linking of both modalities involved source

4.5. Emotion Recognition

Emotion recognition is a great example of why multimodal datasets are preferred over monomodal ones. Emotion recognition can be performed with just monomodal datasets, but performance can improve if multimodal datasets are used as input. The multimodal input can take the form of video + text + audio, but sensor data like encephalogram data can also be incorporated.

Sometimes, using multiple input modalities may degrade performance compared to single modality counterparts, even though a dataset with multiple modalities will always convey more information. This is attributed to the difficulty of training multimodal networks.

5. Multimodal Deep Learning Datasets

Without data, there is no learning. Multimodal machine learning is no exception. To advance the field, researchers and organizations have created and distributed multiple multimodal datasets. Here’s a comprehensive list of the most popular datasets:

• VQA: A Visual Question Answering multimodal dataset containing 265K images (vision) with at least three questions (text) for each image. These questions require an understanding of vision, language, and commonsense knowledge to answer. Suitable for visual-question answering and image captioning.
• Social-IQ: A multimodal dataset to train deep learning models on visual reasoning, multimodal question answering, and social interaction understanding. Contains 1250 audio videos rigorously annotated (on the action level) with questions and answers (text) related to the actions taking place in each scene.
• RGB-D Object Dataset: A multimodal dataset that combines visual and sensor modalities. One sensor is RGB and encodes colors in a picture, while the other is a depth sensor that encodes the distance of an object from the camera. This dataset contains videos of 300 household objects and 22 scenes, equal to 250K images. It has been used for 3D object detection or depth estimation tasks.

Other multimodal datasets include IEMOCAP, CMU-MOSI, MPI-SINTEL, SCENE-FLOW, HOW2, COIN, and MOUD.

LEARNS.EDU.VN offers resources and guidance on utilizing these datasets to build and train effective multimodal models.

6. Key Takeaways

Multimodal deep learning is a significant step toward creating more powerful AI models. Datasets with multiple modalities convey more information than unimodal datasets, so machine learning models should theoretically improve their predictive performance by processing multiple input modalities. However, the challenges and difficulties of training multimodal networks often pose a barrier to improving performance.

Nonetheless, multimodal applications open a new world of possibilities for AI. Some tasks that humans perform well are only possible when models incorporate multiple modalities into their training. Multimodal deep learning is an active research area with applications in multiple fields.

FAQ Section

Q1: What is multimodal learning in machine learning?

Multimodal learning is a machine learning approach that trains AI models to process and relate different data types, such as images, text, and audio, to understand and interact with the world more like humans do.

Q2: What are the primary modalities used in multimodal learning?

The primary modalities include visual data (images, videos), textual data, and auditory data (voice, sounds, music). Additional modalities can include 3D data, depth sensor data, and sensor readings like thermal or EEG data.

Q3: What are the main challenges in multimodal learning?

The main challenges include representation (encoding multimodal data effectively), fusion (combining information from different modalities), alignment (identifying relationships between modalities), translation (mapping one modality to another), and co-learning (transferring knowledge between modalities).

Q4: How does multimodal learning improve AI capabilities?

By combining different data types, multimodal learning enables AI models to gain a more comprehensive understanding of their environment, leading to improved performance in tasks such as emotion recognition, image captioning, and visual question answering.

Q5: What are some applications of multimodal deep learning?

Applications include image captioning (generating text descriptions for images), image retrieval (finding relevant images based on a query), text-to-image generation (creating images from text descriptions), visual question answering (answering questions about images), and emotion recognition.

Q6: What is the difference between unimodal and multimodal learning?

Unimodal learning involves processing only a single type of data, while multimodal learning involves processing multiple types of data to gain a more complete understanding.

Q7: What is multimodal fusion?

Multimodal fusion is the process of combining information from two or more modalities to perform a prediction task. It is a core challenge in multimodal learning, involving techniques like concatenation, weighted sums, and attention mechanisms.

Q8: What role do neural networks play in multimodal learning?

Neural networks, particularly deep neural networks, are commonly used in multimodal learning to encode, fuse, and classify data from different modalities. They help in capturing complex relationships between the data types.

Q9: Can multimodal learning improve emotion recognition?

Yes, multimodal learning can significantly improve emotion recognition by combining visual cues (facial expressions) with auditory cues (tone of voice) and textual cues (context), leading to more accurate results.

Q10: Where can I learn more about multimodal learning?

You can explore courses and resources at LEARNS.EDU.VN to gain in-depth knowledge and practical skills in multimodal learning.

Ready to dive deeper into the world of multimodal learning? Visit LEARNS.EDU.VN to explore our courses and resources designed to help you master this cutting-edge field. Whether you’re looking to understand the basics, tackle advanced techniques, or explore real-world applications, we have the tools and expertise to guide you on your journey. Contact us at 123 Education Way, Learnville, CA 90210, United States. Whatsapp: +1 555-555-1212 or visit our website at learns.edu.vn for more information.