# Enhancing Image Retrieval : A Comprehensive Study on Photo Search using the CLIP Mode

arXiv: https://arxiv.org/abs/2401.13613 · HTML: https://mlsanity.com/paper/2401.13613/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 1 references that are in the map.
- First posted on arXiv 24 Jan 2024.

## Built on by (most cited first, 6 of 6)

- [BendVLM: Test-Time Debiasing of Vision-Language Embeddings](https://mlsanity.com/paper/2411.04420/) (2024)
- [ECOR: Explainable CLIP for Object Recognition](https://mlsanity.com/paper/2404.12839/) (2024)
- [Semantic Approach to Quantifying the Consistency of Diffusion Model Image Generation](https://mlsanity.com/paper/2404.08799/) (2024)
- [Meme Similarity and Emotion Detection using Multimodal Analysis](https://mlsanity.com/paper/2503.17493/) (2025)
- [Multi-method Integration with Confidence-based Weighting for Zero-shot Image Classification](https://mlsanity.com/paper/2405.02155/) (2024)
- [Robust Vision-Language Models via Tensor Decomposition: A Defense Against Adversarial Attacks](https://mlsanity.com/paper/2509.16163/) (2025)

## Abstract (arXiv)

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