Text-to-image diffusion models have made significant advancements in generating high-quality, diverse images from text prompts. However, the inherent limitations of textual signals often prevent these models from fully capturing specific concepts, thereby reducing their controllability. To address this issue, several approaches have incorporated personalization techniques, utilizing reference images to mine visual concept representations that complement textual inputs and enhance the controllability of text-to-image diffusion models. Despite these advances, a comprehensive, systematic exploration of visual concept mining remains limited. In this paper, we categorize existing research into four key areas: Concept Learning, Concept Erasing, Concept Decomposition, and Concept Combination. This classification provides valuable insights into the foundational principles of Visual Concept Mining (VCM) techniques. Additionally, we identify key challenges and propose future research directions to propel this important and interesting field forward.
@article{arxiv.2503.13576,
title = {A Comprehensive Survey on Visual Concept Mining in Text-to-image Diffusion Models},
author = {Ziqiang Li and Jun Li and Lizhi Xiong and Zhangjie Fu and Zechao Li},
journal= {arXiv preprint arXiv:2503.13576},
year = {2025}
}