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Multimodal Deep Learning and its Applications

As humans, our perception of the world is through our senses. We identify objects or anything through vision, sound, touch, and odor. Our way of processing this sensory information is multimodal. Modality refers to the way something is recognized, experienced, and recorded. Multimodal deep learning is an extensive research branch in Deep learning that works on the fusion of multimodal data.

The human brain consists of millions of neural networks that process multiple modalities from the external world. It could be recognizing a persons body movements, tone of voice, or even mimicking sounds. For AI to interpret Human Intelligence, we need a reasonable fusion of multimodal data and this is done through Multimodal Deep Learning.

Multimodal Machine Learning is developing computer algorithms that learn and predict using Multimodal datasets.

Multimodal Deep learning is a subset of the machine learning branch. With this technology, AI models are trained to identify relationships between multiple modalities such as images, videos, and texts and provide accurate predictions. From identifying the relevant link between datasets, Deep Learning models will be able to capture any place's environment and a person's emotional state.

If we say, Unimodal models that interpret only a single dataset have proven efficient in computer vision and Natural Language Processing. Unimodal models have limited capabilities; in certain tasks, these models failed to recognize humor, sarcasm, and hate speech. Whereas, Multimodal learning models can be referred to as a combination of unimodal models.

Multimodal deep learning includes modalities like visual, audio, and textual datasets. 3D visual and LiDAR data are slightly used multimodal data.

Multimodal Learning models work on the fusion of multiple unimodal neural networks.

First unimodal neural networks process the data separately and encode them, later, the encoded data is extracted and fused. Multimodal data fusion is an important process carried out using multiple fusion techniques. Finally, with the fusion of multimodal data, neural networks recognize and predict the outcome of the input key.

For example, in any video, there might be two unimodal models visual data and audio data. The perfect synchronization of both unimodal datasets provides simultaneous working of both models.

Fusing multimodal datasets improves the accuracy and robustness of Deep learning models, enhancing their performance in real-time scenarios.

Multimodal Deep learning has potential applications in computer vision algorithms. Here are some of its applications;

The research to reduce human efforts and develop machines matching with human intelligence is enormous. This requires multimodal datasets that can be combined using Machine Learning and Deep Learning models, paving the way for more advanced AI tools.

The recent surge in the popularity of AI tools has brought more additional investments in Artificial Intelligence and Machine Learning technology. This is a great time to grab job opportunities by learning and upskilling yourself in Artificial Intelligence and Machine Learning.

Read more:
Multimodal Deep Learning - A Fusion of Multiple Modalities - NASSCOM Community