让安卓梦见电羊：一种受人类启发的图像隐喻理解和推理框架
Let Androids Dream of Electric Sheep: A Human-Inspired Image Implication Understanding and Reasoning Framework
AI总结：
本研究提出LAD框架，通过三阶段方法解决图像隐喻理解问题，在多个基准测试中取得优异成绩，推动视觉语言推理和人机交互发展。
摘要：
图像中的隐喻理解仍然是人工智能系统面临的关键挑战，因为现有模型难以把握嵌入在视觉内容中的复杂文化、情感和情境含义。虽然多模态大语言模型（MLLMs）在一般的视觉问答（VQA）任务中表现优异，但它们在图像隐喻任务上存在根本性的局限性：即上下文缺失会掩盖不同视觉元素及其抽象含义之间的关系。受人类认知过程的启发，我们提出了Let Androids Dream（LAD），一种新的图像隐喻理解和推理框架。LAD通过三阶段框架解决上下文缺失问题：（1）感知：将视觉信息转换为丰富且多层次的文本表示；（2）搜索：迭代搜索和整合跨领域知识以解决歧义；（3）推理：通过显式推理生成上下文对齐的图像隐喻。我们的框架使用轻量级的GPT-4o-mini模型，在英语图像隐喻基准上实现了优于15个以上MLLMs的最先进性能，并在中文基准上取得了显著提升，在多项选择题（MCQ）任务中表现与Gemini-3.0-pro模型相当，在开放式问题（OSQ）任务中超越GPT-4o模型36.7%。泛化实验还显示，我们的框架能有效促进一般VQA和视觉推理任务。此外，我们的工作为AI如何更有效地解释图像隐喻提供了新的见解，推动了视觉语言推理和人机交互领域的进展。我们的项目在https://github.com/MING-ZCH/Let-Androids-Dream-of-Electric-Sheep上公开发布。
英文摘要：
Metaphorical comprehension in images remains a critical challenge for AI systems, as existing models struggle to grasp the nuanced cultural, emotional, and contextual implications embedded in visual content. While multimodal large language models (MLLMs) excel in general Visual Question Answer (VQA) tasks, they struggle with a fundamental limitation on image implication tasks: contextual gaps that obscure the relationships between different visual elements and their abstract meanings. Inspired by the human cognitive process, we propose Let Androids Dream (LAD), a novel framework for image implication understanding and reasoning. LAD addresses contextual missing through the three-stage framework: (1) Perception: converting visual information into rich and multi-level textual representations, (2) Search: iteratively searching and integrating cross-domain knowledge to resolve ambiguity, and (3) Reasoning: generating context-alignment image implication via explicit reasoning. Our framework with the lightweight GPT-4o-mini model achieves SOTA performance compared to 15+ MLLMs on English image implication benchmark and a huge improvement on Chinese benchmark, performing comparable with the Gemini-3.0-pro model on Multiple-Choice Question (MCQ) and outperforms the GPT-4o model 36.7% on Open-Style Question (OSQ). Generalization experiments also show that our framework can effectively benefit general VQA and visual reasoning tasks. Additionally, our work provides new insights into how AI can more effectively interpret image implications, advancing the field of vision-language reasoning and human-AI interaction. Our project is publicly available at https://github.com/MING-ZCH/Let-Androids-Dream-of-Electric-Sheep.
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。
