CLIP Model for Images to Textual Prompts Based on Top-k Neighbors
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917570116845568 |
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| author | Zhang, Xin Zhang, Xin Cai, YeMing Jia, Tianzhi |
| author_facet | Zhang, Xin Zhang, Xin Cai, YeMing Jia, Tianzhi |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2401_09763 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CLIP Model for Images to Textual Prompts Based on Top-k Neighbors Zhang, Xin Zhang, Xin Cai, YeMing Jia, Tianzhi Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | CLIP Model for Images to Textual Prompts Based on Top-k Neighbors |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2401.09763 |