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Semantic clustering under resource constraints via Bayesian low-rank adaptation.

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Summary

This study introduces a novel framework for image clustering using vision-language models (VLMs). It enhances semantic understanding in low-resource settings, improving clustering accuracy and robustness.

Keywords:
Bayesian LoRALLMSemantic clusteringVLM

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional image clustering relies on visual similarity, limiting semantic understanding.
  • Vision-Language Models (VLMs) enable semantics-aware clustering but struggle in low-resource scenarios due to noisy outputs.
  • Small-scale VLMs exhibit semantic inconsistency, compromising text-based image representations.

Purpose of the Study:

  • To develop a user-guided multimodal semantic clustering framework for low-resource, user-defined scenarios.
  • To enhance the quality of text-based representations from small-scale VLMs.
  • To improve the accuracy and robustness of image clustering with reduced computational overhead.

Main Methods:

  • Utilizing a visual question answering VLM for image description generation.
  • Employing a small-scale large language model for user-defined semantic classification.
  • Implementing token-level confidence estimation for adaptive feature weighting and robust fusion.
  • Introducing a Bayesian low-rank adaptation alignment module for cross-modal projection with structured sparsity.

Main Results:

  • Consistent improvements in accuracy and robustness for user-defined semantic clustering tasks.
  • Competitive performance on standard category clustering benchmarks.
  • Substantially reduced computational overhead compared to large-scale VLMs.
  • Demonstrated effectiveness in low-resource, user-defined semantic clustering scenarios.

Conclusions:

  • The proposed framework offers a promising direction for controllable, interpretable, and scalable clustering in practical multimodal settings.
  • It effectively mitigates issues of noisy generation and semantic inconsistency in small-scale VLMs.
  • The method provides a viable solution for resource-constrained environments requiring semantic image clustering.