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IE-MAS: Internal-External Multi-Agent Steering for Controllable Image Captioning.

Tiecheng Cai1, Chao Chen2, Shanshan Lin1

  • 1College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China.

Entropy (Basel, Switzerland)
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces Internal-External Multi-Agent Steering (IE-MAS) to improve Controllable Image Captioning (CIC). IE-MAS effectively manages multiple constraints like length and sentiment for better image descriptions.

Keywords:
affective analysiscontrollable image captioninginformation theorymulti-agent systems

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Natural Language Processing

Background:

  • Controllable Image Captioning (CIC) aims to generate image descriptions adhering to specific user constraints.
  • Existing methods struggle to simultaneously satisfy multiple constraints due to interference.
  • Challenges include balancing semantic faithfulness, affective expression, and length control.

Purpose of the Study:

  • To propose a novel method, Internal-External Multi-Agent Steering (IE-MAS), for addressing the limitations of current CIC approaches.
  • To enable the generation of image captions that satisfy multiple, potentially interacting, constraints.
  • To improve the coherence, faithfulness, and expressiveness of generated captions.

Main Methods:

  • IE-MAS employs an internal multimodal steering (IMS) strategy for affective coherence control.
  • An external multi-agent collaboration system (EMCS) is utilized for visual grounding and contextual alignment.
  • The approach balances internal linguistic control and external perceptual grounding via adaptive steering.

Main Results:

  • IE-MAS effectively coordinates multiple constraints in image caption generation.
  • Generated captions satisfy length constraints while being sentimentally expressive and visually faithful.
  • The method demonstrates improved performance in balancing semantic consistency, affective expression, and length control.

Conclusions:

  • IE-MAS offers a robust solution for multi-constraint Controllable Image Captioning.
  • The proposed IMS and EMCS strategies successfully manage complex control interactions.
  • This work advances the state-of-the-art in generating contextually relevant and stylistically controlled image descriptions.