Image Inference through Multi-Modal LLM Models

Author(s): Chinmay Bhalerao

Originally published on Towards AI.

This blog explores the capabilities of multi-modal models in image inference, highlighting their ability to integrate visual and textual information for improved analysis

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The emergence of multimodal AI has significantly transformed the landscape of data wrangling. In the past, we relied heavily on text extraction libraries like PyTesseract for tasks such as optical character recognition (OCR). However, the advancement of Vision Transformers and other multimodal models has revolutionized how we process and interpret data. These advanced models are capable of seamlessly integrating information from multiple modalities, such as images and text, providing a more holistic and efficient approach to data extraction and interpretation. This shift has paved the way for more accurate and sophisticated AI-driven solutions across various industries.

We will start with the actual and important question.

What is meant by MULTI-MODAL?

To help you understand this, I will give you a snippet from Wikipedia.

In simple words, when there is more than one mode of communication, it is said to be multimodal. To understand this, let's take the example of multimodal communication.

Multimodal pedagogy is an approach to the teaching of writing that implements different modes of communication.Multimodality refers to the use of visual, aural, linguistic, spatial, and gestural modes in differing pieces of media, each necessary to properly convey the information it presents.

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Published via Towards AI