CLIP Model for Images to Textual Prompts Based on Top-k Neighbors
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 10 of its 11 references that are in the map. First posted on arXiv 18 Jan 2024.
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10 of 11 references.
References · 10
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