让个性化图像生成模型的调整细节可被自然语言精准解释。
Deciphering Personalization: Towards Fine-Grained Explainability in Natural Language for Personalized Image Generation Models
- 提出细粒度自然语言解释技术 FineXL，逐项分析个性化维度。
- 可量化每个个性化维度的程度，准确率提升 56%。
- 适合需要透明化生成逻辑的开发者与普通用户。
图像生成模型在实际应用中常需个性化以满足用户多样化需求，但现有方法缺乏对个性化过程的可解释性。虽可通过生成图像中的视觉特征提供解释，但人类难以理解。自然语言解释更具优势，但当前方法仅能粗粒度描述，无法精确识别多个个性化方面及其程度差异。为此，本文提出新方法 FineXL，实现个性化图像生成模型的细粒度自然语言可解释性。FineXL 能对每一项个性化维度生成自然语言描述，并附带量化评分。实验表明，在多种个性化场景和不同类型的图像生成模型上，其解释准确率提升 56%。
原文摘要 · Abstract (English)
Image generation models are usually personalized in practical uses in order to better meet the individual users' heterogeneous needs, but most personalized models lack explainability about how they are being personalized. Such explainability can be provided via visual features in generated images, but is difficult for human users to understand. Explainability in natural language is a better choice, but the existing approaches to explainability in natural language are limited to be coarse-grained. They are unable to precisely identify the multiple aspects of personalization, as well as the varying levels of personalization in each aspect. To address such limitation, in this paper we present a new technique, namely \textbf{FineXL}, towards \textbf{Fine}-grained e\textbf{X}plainability in natural \textbf{L}anguage for personalized image generation models. FineXL can provide natural language descriptions about each distinct aspect of personalization, along with quantitative scores indicating the level of each aspect of personalization. Experiment results show that FineXL can improve the accuracy of explainability by 56\%, when different personalization scenarios are applied to multiple types of image generation models.
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