Humans use all five senses to experience and interpret the world around them. Our five senses capture information from five different sources and in five different ways. A modality refers to the way something happens, is experienced, or is captured.
The human brain consists of neural networks that can process multiple modalities simultaneously. Imagine having a conversation - your brain's neural networks process multimodal input (audio, sight, text, smell). After deep subconscious mode fusion, you can infer what the other person is saying, their emotional state, and their surroundings. This allows for a more complete picture and a deeper understanding of the situation.
For AI to match human intelligence, it must learn to interpret, reason, and fuse multimodal information. One of the latest and most promising trends in deep learning research is multimodal deep learning. In this paper, we demystify multimodal deep learning. We discuss multimodal fusion, multimodal datasets, multimodal applications, and explain how to build machine learning models that more fully perceive the world.
What is Multimodal Deep Learning ?
Multimodal machine learning is the study of computer algorithms that learn and improve performance by using multimodal datasets.
Multimodal deep learning is a subfield of machine learning that aims to train artificial intelligence models to process and discover relationships between different types of data (patterns)—typically images, video, audio, and text. By combining different modalities, a deep learning model can understand its environment more generally, since certain cues are only present in certain modalities. Imagine the task of emotion recognition. It's not just looking at a human face (visual modality). The pitch and pitch of a person's voice (audio modality) encode a wealth of information about their emotional state that may not be seen through their facial expressions, even though they are often in sync.
Multimodal models typically rely on deep neural networks, although other machine learning models such as hidden Markov models (HMMs) or restricted Boltzmann machines (RBMs) have been incorporated into earlier studies.
In multimodal deep learning, the most typical modalities are visual (image, video), textual and auditory (speech, sound, music). However, other less typical modalities include 3D vision data, depth sensor data, and LiDAR data (typical in autonomous vehicles). In clinical practice, imaging modalities include computed tomography (CT) scans and X-ray images, while non-imaging modalities include electroencephalogram (EEG) data. Sensor data such as thermal data or data from eye-tracking devices can also be included in the list.
The Importance of Data Annotation for Multimodal Deep Learning
Data annotation plays a crucial role in multimodal deep learning, which is the basis of model training. First, multimodal deep learning requires many types of data, such as images, texts, speech, etc. These data must be labeled to be used by the model for learning. The purpose of labeling is to make the model understand the meaning of the data so that data of different modalities can be connected together for horizontal or vertical integration.
Data annotation can help the model learn to identify and understand data of different modalities more accurately and efficiently. For example, in an image recognition task, annotations can tell the model which regions should be recognized as part of an object, and which regions should be excluded. In natural language processing, annotations can help models learn to recognize entities, relationships, and semantics in text
Data annotation can also help deep learning models to optimize and tune. Annotated data can help the model find mistakes and adjust accordingly to achieve better results. In addition, annotation can also help the model to perform different types of learning methods such as supervised learning, semi-supervised learning, and self-supervised learning to adapt to different task requirements.
Multimodal deep learning is a step toward 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 handling multiple input modalities. However, the challenges and difficulties of training multimodal networks often pose a barrier to improving performance.
Nonetheless, multimodal applications open up a new world of possibilities for artificial intelligence. Certain tasks that humans can be very good at are only possible if the model incorporates multiple modalities into its training. Multimodal deep learning is a very active research area with applications in several fields.
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