让图像处理系统像专家一样灵活调用工具，突破单一模型的局限。
Position: Agentic Systems Constitute a Key Component of Next-Generation Intelligent Image Processing
- 设计能自主选择、组合工具的智能代理系统
- 解决传统模型泛化差、适应性弱的问题
- 适合想提升图像处理灵活性的研究者
本文主张图像处理领域应从以模型为中心的范式，转向包含智能代理系统设计的互补范式。尽管深度学习在特定任务上取得显著进展，当前方法在泛化能力、适应性和现实问题求解灵活性方面仍存在关键短板。我们提出，发展能够动态选择、组合和优化现有图像处理工具的智能代理系统，是该领域的下一步演进方向。此类系统可模拟人类专家策略性地协调多种工具解决复杂问题，克服单体模型的脆弱性。论文分析了模型主导范式的局限性，确立了智能图像处理系统的设计原则，并提出了不同能力层级的代理系统框架。
原文摘要 · Abstract (English)
This position paper argues that the image processing community should broaden its focus from purely model-centric development to include agentic system design as an essential complementary paradigm. While deep learning has significantly advanced capabilities for specific image processing tasks, current approaches face critical limitations in generalization, adaptability, and real-world problem-solving flexibility. We propose that developing intelligent agentic systems, capable of dynamically selecting, combining, and optimizing existing image processing tools, represents the next evolutionary step for the field. Such systems would emulate human experts' ability to strategically orchestrate different tools to solve complex problems, overcoming the brittleness of monolithic models. The paper analyzes key limitations of model-centric paradigms, establishes design principles for agentic image processing systems, and outlines different capability levels for such agents.
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