As soon as you stick your head out the window, a lot of information comes to you. This has to do with the combination of our five senses. hearing, sight, smell, taste, touch, This allows us to perceive sounds, images, textures, and scents all at the same time.
Multimodal learning aims to apply this idea of using different data simultaneously to the field of AI. Let's start by looking at the different types of sauces.
Text is one of the most commonly used modalities in machine learning. Text data contains rich structured information from which knowledge can be easily extracted using natural language processing (NLP).
This data can come from documents, press articles, messages on social networks, or other types of text. NLP techniques used to process them include tokenization, lemmatization, syntactic analysis, named entity detection, and text classification.
Images are an important source of visual information in multimodal learning. Thanks to the proliferation of convolutional neural networks (CNNs), significant advances have been made in image understanding.
Computer vision techniques can be used to analyze and interpret images to extract knowledge. Examples include object detection, face recognition, and image segmentation.
Audio includes information from voice recordings, sound files, or live streams. These are analyzed using audio processing techniques to extract acoustic and linguistic features.
The most commonly used methods include speech recognition, sound event detection, and sound source separation and classification.
Finally, video is a powerful source of multimodal data that combines visual and audio information. Once again, computer vision and audio processing techniques can be used to extract knowledge from sequences.
This makes it possible to detect moving objects, analyze human activity, and even recognize gestures. This fusion of visual and audio modalities allows machines to better understand scenes and events.
The rise of smartphone cameras and video-sharing social networks like TikTok and YouTube has given AI access to vast resources for training.
In the future, Emergence of humanoid robots Artificial intelligence equipped with tactile sensors on its fingers can also receive tactile sensations and use them for learning.
