Let Androids Dream of Electric Sheep: A Human-like Image Implication
  Understanding and Reasoning Framework
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.
683041f868160a3c0e525cae
Chenhao Zhang
claimed_verified
2025-05-26T08:14:25.611Z
647daf00cfca67bc50f9a99f
https://cdn-avatars.huggingface.co/v1/production/uploads/647daf00cfca67bc50f9a99f/8Snmk1V6lZ8ecdTW3POfm.jpeg
Chenhao(Leo) Zhang
user
MING-ZCH
683041f868160a3c0e525caf
Yazhe Niu
Let Androids Dream of Electric Sheep: A Human-like Image Implication
  Understanding and Reasoning Framework
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.
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.
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)