# Customized Multiple Clustering via Multi-Modal Subspace Proxy Learning

arXiv: https://arxiv.org/abs/2411.03978 · HTML: https://mlsanity.com/paper/2411.03978/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 39 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 2 of 2)

- [Visual Classification via Description from Large Language Models](https://mlsanity.com/paper/2210.07183/) (2022)
- [Unsupervised Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2204.03649/) (2022)

## Built on by (most cited first, 12 of 12)

- [Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding](https://mlsanity.com/paper/2502.06020/) (2025)
- [Music Audio-Visual Question Answering Requires Specialized Multimodal Designs](https://mlsanity.com/paper/2505.20638/) (2025)
- [Learning Sparsity for Effective and Efficient Music Performance Question Answering](https://mlsanity.com/paper/2506.01319/) (2025)
- [Open Ad-hoc Categorization with Contextualized Feature Learning](https://mlsanity.com/paper/2512.16202/) (2025)
- [Agent-Centric Personalized Multiple Clustering with Multi-Modal LLMs](https://mlsanity.com/paper/2503.22241/) (2025)
- [ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering](https://mlsanity.com/paper/2512.00725/) (2025)
- [Multi-modal Dynamic Proxy Learning for Personalized Multiple Clustering](https://mlsanity.com/paper/2511.07274/) (2025)
- [Semantic Purification for Conditional Representation Learning](https://mlsanity.com/paper/2602.05464/) (2026)
- [Language-Assisted Image Clustering Guided by Discriminative Relational Signals and Adaptive Semantic Centers](https://mlsanity.com/paper/2603.24275/) (2026)
- [Edu-MMBias: A Three-Tier Multimodal Benchmark for Auditing Social Bias in Vision-Language Models under Educational Contexts](https://mlsanity.com/paper/2604.10200/) (2026)
- [Universal Guideline-Driven Image Clustering via a Hybrid LLM Agent](https://mlsanity.com/paper/2606.24094/) (2026)
- [Controlling Embedding Spaces with Text-Conditioned Transformations](https://mlsanity.com/paper/2607.22919/) (2026)

## Abstract (arXiv)

Multiple clustering aims to discover various latent structures of data from different aspects. Deep multiple clustering methods have achieved remarkable performance by exploiting complex patterns and relationships in data. However, existing works struggle to flexibly adapt to diverse user-specific needs in data grouping, which may require manual understanding of each clustering. To address these limitations, we introduce Multi-Sub, a novel end-to-end multiple clustering approach that incorporates a multi-modal subspace proxy learning framework in this work. Utilizing the synergistic capabilities of CLIP and GPT-4, Multi-Sub aligns textual prompts expressing user preferences with their corresponding visual representations. This is achieved by automatically generating proxy words from large language models that act as subspace bases, thus allowing for the customized representation of data in terms specific to the user's interests. Our method consistently outperforms existing baselines across a broad set of datasets in visual multiple clustering tasks. Our code is available at https://github.com/Alexander-Yao/Multi-Sub.