Data Preprocessing for Multimodal AI

Описание: Preprocessing is a critical step in building multimodal AI models. It involves cleaning and transforming raw data into a format suitable for model training, ensuring that the data is consistent and compatible across different modalities. For text data, preprocessing typically includes steps like tokenization, where text is broken down into individual words or tokens, and word embedding, where words are converted into numerical vectors. This helps the model understand and process natural language effectively. Vision data, which involves images and videos, often requires resizing, normalization, and sometimes data augmentation to enhance model performance. Images need to be standardized in terms of size and pixel values to ensure consistency. Audio data preprocessing includes tasks like noise reduction, feature extraction, and sometimes conversion into spectrograms. These steps help in isolating relevant audio features that the AI model can learn from. The ultimate goal of preprocessing is to create a dataset where each modality is compatible with the others, allowing for seamless integration and effective model training.