title: Let Androids Dream of Electric Sheep: A Human-like Image Implication
    Understanding and Reasoning Framework
summary: 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 basic 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 GPT-4o model on Multiple-Choice Question (MCQ)
  and outperforms 36.7% on Open-Style Question (OSQ). 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.
paper_authors._id: 683041f868160a3c0e525cae
paper_authors.name: Chenhao Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:14:25.611Z
paper_authors._id: 647daf00cfca67bc50f9a99f
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/647daf00cfca67bc50f9a99f/8Snmk1V6lZ8ecdTW3POfm.jpeg
paper_authors.fullname: Chenhao(Leo) Zhang
paper_authors.type: user
paper_authors.user: MING-ZCH
paper_authors._id: 683041f868160a3c0e525caf
paper_authors.name: Yazhe Niu
paper_title: Let Androids Dream of Electric Sheep: A Human-like Image Implication
    Understanding and Reasoning Framework
paper_summary: 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 basic 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 GPT-4o model on Multiple-Choice Question (MCQ)
  and outperforms 36.7% on Open-Style Question (OSQ). 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.
paper_ai_summary: LAD, a three-stage framework using GPT-4o-mini, achieves state-of-the-art performance in image implication understanding and reasoning tasks across different languages and question types.
paper_ai_keywords: Visual Question Answering (VQA) image implication understanding reasoning multimodal large language models (MLLMs) cross-domain knowledge context-alignment Multiple-Choice Question (MCQ) Open-Style Question (OSQ)