Language-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908913247453184 |
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| author | Ma, Jun Zhang, Xu Jiao, Zhengxing Hou, Yaxin Liu, Hui Hou, Junhui Jia, Yuheng |
| author_facet | Ma, Jun Zhang, Xu Jiao, Zhengxing Hou, Yaxin Liu, Hui Hou, Junhui Jia, Yuheng |
| contents | Language-Assisted Image Clustering (LAIC) augments the input images with additional texts with the help of vision-language models (VLMs) to promote clustering performance. Despite recent progress, existing LAIC methods often overlook two issues: (i) textual features constructed for each image are highly similar, leading to weak inter-class discriminability; (ii) the clustering step is restricted to pre-built image-text alignments, limiting the potential for better utilization of the text modality. To address these issues, we propose a new LAIC framework with two complementary components. First, we exploit cross-modal relations to produce more discriminative self-supervision signals for clustering, as it compatible with most VLMs training mechanisms. Second, we learn category-wise continuous semantic centers via prompt learning to produce the final clustering assignments. Extensive experiments on eight benchmark datasets demonstrate that our method achieves an average improvement of 2.6% over state-of-the-art methods, and the learned semantic centers exhibit strong interpretability. Code is available in the supplementary material. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_24275 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Language-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers Ma, Jun Zhang, Xu Jiao, Zhengxing Hou, Yaxin Liu, Hui Hou, Junhui Jia, Yuheng Machine Learning Language-Assisted Image Clustering (LAIC) augments the input images with additional texts with the help of vision-language models (VLMs) to promote clustering performance. Despite recent progress, existing LAIC methods often overlook two issues: (i) textual features constructed for each image are highly similar, leading to weak inter-class discriminability; (ii) the clustering step is restricted to pre-built image-text alignments, limiting the potential for better utilization of the text modality. To address these issues, we propose a new LAIC framework with two complementary components. First, we exploit cross-modal relations to produce more discriminative self-supervision signals for clustering, as it compatible with most VLMs training mechanisms. Second, we learn category-wise continuous semantic centers via prompt learning to produce the final clustering assignments. Extensive experiments on eight benchmark datasets demonstrate that our method achieves an average improvement of 2.6% over state-of-the-art methods, and the learned semantic centers exhibit strong interpretability. Code is available in the supplementary material. |
| title | Language-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2603.24275 |