MoralCLIP：基于道德基础理论的视觉-语言表征对比对齐
MoralCLIP: Contrastive Alignment of Vision-and-Language Representations with Moral Foundations Theory
AI总结：
针对视觉语言模型缺乏道德维度理解能力的问题，提出基于道德基础理论的MoralCLIP方法，通过道德数据增强训练实现跨模态道德对齐，提升多模态道德内容理解能力。
摘要：
视觉语言模型的最新进展已实现跨模态的丰富语义理解，但这些编码方法缺乏解读或推理内容道德维度的能力——这是人类认知的关键层面。本文针对这一空白，提出MoralCLIP，一种新型嵌入表征方法，基于道德基础理论（Moral Foundations Theory, MFT）为多模态学习引入显式道德基础。该方法将视觉和文本道德线索整合到统一嵌入空间，实现跨模态道德对齐。MoralCLIP以多标签数据集Social-Moral Image Database为基础，识别视觉内容中共同出现的道德基础。在训练阶段，我们设计了一种道德数据增强策略，将标注数据集扩展至15000个标注有MFT对齐维度的图文对。结果表明，显式道德监督可提升单模态和多模态对道德内容的理解，为能够识别并对齐人类道德价值观的道德感知AI系统奠定基础。
英文摘要：
Recent advances in vision-language models have enabled rich semantic understanding across modalities. However, these encoding methods lack the ability to interpret or reason about the moral dimensions of content-a crucial aspect of human cognition. In this paper, we address this gap by introducing MoralCLIP, a novel embedding representation method that extends multimodal learning with explicit moral grounding based on Moral Foundations Theory (MFT). Our approach integrates visual and textual moral cues into a unified embedding space, enabling cross-modal moral alignment. MoralCLIP is grounded on the multi-label dataset Social-Moral Image Database to identify co-occurring moral foundations in visual content. For MoralCLIP training, we design a moral data augmentation strategy to scale our annotated dataset to 15,000 image-text pairs labeled with MFT-aligned dimensions. Our results demonstrate that explicit moral supervision improves both unimodal and multimodal understanding of moral content, establishing a foundation for morally-aware AI systems capable of recognizing and aligning with human moral values.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
