Image Clustering Conditioned on Text Criteria
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866907807386697728 |
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| author | Kwon, Sehyun Park, Jaeseung Kim, Minkyu Cho, Jaewoong Ryu, Ernest K. Lee, Kangwook |
| author_facet | Kwon, Sehyun Park, Jaeseung Kim, Minkyu Cho, Jaewoong Ryu, Ernest K. Lee, Kangwook |
| contents | Classical clustering methods do not provide users with direct control of the clustering results, and the clustering results may not be consistent with the relevant criterion that a user has in mind. In this work, we present a new methodology for performing image clustering based on user-specified text criteria by leveraging modern vision-language models and large language models. We call our method Image Clustering Conditioned on Text Criteria (IC|TC), and it represents a different paradigm of image clustering. IC|TC requires a minimal and practical degree of human intervention and grants the user significant control over the clustering results in return. Our experiments show that IC|TC can effectively cluster images with various criteria, such as human action, physical location, or the person's mood, while significantly outperforming baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_18297 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Image Clustering Conditioned on Text Criteria Kwon, Sehyun Park, Jaeseung Kim, Minkyu Cho, Jaewoong Ryu, Ernest K. Lee, Kangwook Computer Vision and Pattern Recognition Artificial Intelligence Classical clustering methods do not provide users with direct control of the clustering results, and the clustering results may not be consistent with the relevant criterion that a user has in mind. In this work, we present a new methodology for performing image clustering based on user-specified text criteria by leveraging modern vision-language models and large language models. We call our method Image Clustering Conditioned on Text Criteria (IC|TC), and it represents a different paradigm of image clustering. IC|TC requires a minimal and practical degree of human intervention and grants the user significant control over the clustering results in return. Our experiments show that IC|TC can effectively cluster images with various criteria, such as human action, physical location, or the person's mood, while significantly outperforming baselines. |
| title | Image Clustering Conditioned on Text Criteria |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2310.18297 |