IE-MAS:用于可控制的图像标题的内部-外部多代理方向控制
Tiecheng Cai1, Chao Chen2, Shanshan Lin1
1College of Computer and Data Science, Fuzhou University, Fuzhou 350108, China.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
本研究介绍了内部-外部多代理方向 (IE-MAS) 以改进可控制图像标题 (CIC). 为了更好的图像描述,IE-MAS有效地管理了长度和情感等多个约束.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 可控制图像标题 (CIC) 旨在生成符合特定用户约束的图像描述.
- 由于干扰,现有的方法难以同时满足多个约束.
- 挑战包括平衡语义忠实,情感表达和长度控制.
研究的目的:
- 为解决当前CIC方法的局限性提出一种新的方法,即内部-外部多代理指导 (IE-MAS),以解决目前CIC方法的局限性.
- 为了使图像标题的生成满足多个,潜在的交互,约束.
- 为了提高生成的标题的连贯性,忠实性和表现力.
主要方法:
- 为了控制情感一致性,IE-MAS采用了内部多式联接方向 (IMS) 策略.
- 外部多代理协作系统 (EMCS) 用于视觉接地和上下文对齐.
- 该方法通过自适应式指导平衡了内部语言控制和外部感知接地.
主要成果:
- IE-MAS有效地协调了图像标题生成中的多个约束.
- 生成的标题满足长度限制,同时具有情感表达力和视觉忠实性.
- 该方法在平衡语义一致性,情感表达和长度控制方面表现得更好.
结论:
- IE-MAS为多限制可控图像标题提供了一个强大的解决方案.
- 拟议的IMS和EMCS战略成功地管理了复杂的控制相互作用.
- 这项工作推进了在生成上下文相关和风格控制的图像描述方面最先进的技术.
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