Self-Enhanced Image Clustering with Cross-Modal Semantic Consistency

TL;DR

This paper introduces a self-enhanced image clustering framework leveraging cross-modal semantic consistency to improve the alignment of pre-trained models with clustering tasks, significantly boosting performance across multiple datasets.

Contribution

It proposes a novel two-stage framework combining cross-modal semantic consistency and self-enhancement to improve image clustering accuracy using pre-trained models.

Findings

Outperforms existing deep clustering methods on six datasets

Achieves or surpasses state-of-the-art accuracy with smaller models

Effectively aligns pre-trained features with clustering objectives

Abstract

While large language-image pre-trained models like CLIP offer powerful generic features for image clustering, existing methods typically freeze the encoder. This creates a fundamental mismatch between the model's task-agnostic representations and the demands of a specific clustering task, imposing a ceiling on performance. To break this ceiling, we propose a self-enhanced framework based on cross-modal semantic consistency for efficient image clustering. Our framework first builds a strong foundation via Cross-Modal Semantic Consistency and then specializes the encoder through Self-Enhancement. In the first stage, we focus on Cross-Modal Semantic Consistency. By mining consistency between generated image-text pairs at the instance, cluster assignment, and cluster center levels, we train lightweight clustering heads to align with the rich semantics of the pre-trained model. This alignment process is bolstered by a novel method for generating higher-quality cluster centers and a dynamic balancing regularizer to ensure well-distributed assignments. In the second stage, we introduce a Self-Enhanced fine-tuning strategy. The well-aligned model from the first stage acts as a reliable pseudo-label generator. These self-generated supervisory signals are then used to feed back the efficient, joint optimization of the vision encoder and clustering heads, unlocking their full potential. Extensive experiments on six mainstream datasets show that our method outperforms existing deep clustering methods by significant margins. Notably, our ViT-B/32 model already matches or even surpasses the accuracy of state-of-the-art methods built upon the far larger ViT-L/14.