This repository contains code and resources for a research project on multimodal search using CLIP (Contrastive Language-Image Pretraining). The goal of this project is to explore and develop techniques that enable searching and retrieving information across multiple modalities, specifically text and images.
Introduction Multimodal search refers to the ability to retrieve relevant information from different modalities, such as text and images. CLIP is a neural network model that learns to associate images and their corresponding textual descriptions. It has shown impressive capabilities in understanding the relationships between different modalities and performing various tasks, including image classification, zero-shot image classification, and text-based image retrieval.
This project aims to leverage CLIP's capabilities to build a multimodal search system. By combining the power of natural language processing (NLP) and computer vision, we can create a system that can understand and process both textual and visual inputs to perform accurate and efficient searches.