Enhancing Image Retrieval : A Comprehensive Study on Photo Search using
  the CLIP Mode
Naresh Kumar Lahajal Harini S
Photo search, the task of retrieving images based on textual queries, has
witnessed significant advancements with the introduction of CLIP (Contrastive
Language-Image Pretraining) model. CLIP leverages a vision-language pre
training approach, wherein it learns a shared representation space for images
and text, enabling cross-modal understanding. This model demonstrates the
capability to understand the semantic relationships between diverse image and
text pairs, allowing for efficient and accurate retrieval of images based on
natural language queries. By training on a large-scale dataset containing
images and their associated textual descriptions, CLIP achieves remarkable
generalization, providing a powerful tool for tasks such as zero-shot learning
and few-shot classification. This abstract summarizes the foundational
principles of CLIP and highlights its potential impact on advancing the field
of photo search, fostering a seamless integration of natural language
understanding and computer vision for improved information retrieval in
multimedia applications