Text-to-image synthesis, a subfield of multimodal generation, has gained significant attention in recent years. We propose a cost-effective approach for image-to-prompt generation that leverages generative models to generate textual prompts without the need for large amounts of annotated data. We divide our method into two stages: online stage and offline stage. We use a combination of the CLIP model and K-nearest neighbors (KNN) algorithm. The proposed system consists of two main parts: an offline task and an online task. Our method owns the highest metric 0.612 among these models, which is 0.013, 0.055, 0.011 higher than Clip, Clip + KNN(top 10) respectively.
@article{arxiv.2401.09763,
title = {CLIP Model for Images to Textual Prompts Based on Top-k Neighbors},
author = {Xin Zhang and Xin Zhang and YeMing Cai and Tianzhi Jia},
journal= {arXiv preprint arXiv:2401.09763},
year = {2024}
}