Related papers: ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering

ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering

URL: http://arxiv.org/abs/2512.00725v1
Date: Sun, 30 Nov 2025 04:36:51 GMT
Title: ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering
Authors: Xinyue Wang, Yuheng Jia, Hui Liu, Junhui Hou,
Abstract summary: Multi-modal large language models (MLLMs) can be leveraged to achieve user-driven clustering.<n>Our method first discovers that MLLMs' hidden states of text tokens are strongly related to the corresponding features.<n>We also employ a lightweight clustering head augmented with pseudo-label learning, significantly enhancing clustering accuracy.
Score: 79.69917150582633
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Typical deep clustering methods, while achieving notable progress, can only provide one clustering result per dataset. This limitation arises from their assumption of a fixed underlying data distribution, which may fail to meet user needs and provide unsatisfactory clustering outcomes. Our work investigates how multi-modal large language models (MLLMs) can be leveraged to achieve user-driven clustering, emphasizing their adaptability to user-specified semantic requirements. However, directly using MLLM output for clustering has risks for producing unstructured and generic image descriptions instead of feature-specific and concrete ones. To address these issues, our method first discovers that MLLMs' hidden states of text tokens are strongly related to the corresponding features, and leverages these embeddings to perform clusterings from any user-defined criteria. We also employ a lightweight clustering head augmented with pseudo-label learning, significantly enhancing clustering accuracy. Extensive experiments demonstrate its competitive performance on diverse datasets and metrics.

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