Abstract
Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts. Unlike concrete concepts that are usually directly associated with physical objects, expressing abstract concepts through natural language requires considerable effort since they are characterized by intricate semantics and connotations. An alternative approach is to leverage images to convey rich visual information as a supplement. Nevertheless, existing Text-to-Image (T2I) models are primarily trained on concrete physical objects and often struggle to visualize abstract concepts. Inspired by the three-layer artwork theory that identifies critical factors, intent, object and form during artistic creation, we propose a framework of Text-to-Image generation for Abstract Concepts (TIAC). The abstract concept is clarified into a clear intent with a detailed definition to avoid ambiguity. LLMs then transform it into semantic-related physical objects, and the concept-dependent form is retrieved from an LLM-extracted form pattern set. Information from these three aspects will be integrated to generate prompts for T2I models via LLM. Evaluation results from human assessments and our newly designed metric concept score demonstrate the effectiveness of our framework in creating images that can sufficiently express abstract concepts.
Citation:
@inproceedings{liao2024tiac,
author = {Jiayi Liao and
Xu Chen and
Qiang Fu and
Lun Du and
Xiangnan He and
Xiang Wang and
Shi Han and
Dongmei Zhang},
title = {Text-to-Image Generation for Abstract Concepts},
booktitle = {{AAAI}},
pages = {3360--3368},
publisher = {{AAAI} Press},
year = {2024}
}
Jiayi Liao
廖佳怡
Xiangnan He
何向南 教授
