CCA：用于图像编辑的协作竞争智能体
CCA: Collaborative Competitive Agents for Image Editing
AI总结：
本文提出多智能体生成模型CCA，通过两个生成器与一个判别器的协作竞争、中间步骤反馈和迭代优化，提升复杂图像编辑指令的生成质量与鲁棒性。
摘要：
本文提出了一种新颖的生成模型——协作竞争智能体（Collaborative Competitive Agents，CCA），它利用多个基于大语言模型（LLM）的智能体的能力来执行复杂任务。受生成对抗网络（GAN）启发，CCA系统采用两个地位平等的生成器智能体和一个判别器智能体。生成器独立处理用户指令并生成结果，而判别器评估输出，并为生成器智能体提供反馈，使其进一步反思并改进生成结果。与以往的生成模型不同，我们的系统能够获得生成的中间步骤。由于这种透明性，每个生成器智能体都可以从其他成功的执行中学习，从而形成一种协作竞争，提升系统结果的质量和鲁棒性。本研究主要聚焦于图像编辑，展示CCA稳健处理复杂指令的能力。本文的主要贡献包括：提出一种基于多智能体、具有可控中间步骤和迭代优化能力的生成模型；对智能体关系进行详细考察；以及在图像编辑方面开展全面实验。代码可在https://github.com/TiankaiHang/CCA获取。
英文摘要：
This paper presents a novel generative model, Collaborative Competitive Agents (CCA), which leverages the capabilities of multiple Large Language Models (LLMs) based agents to execute complex tasks. Drawing inspiration from Generative Adversarial Networks (GANs), the CCA system employs two equal-status generator agents and a discriminator agent. The generators independently process user instructions and generate results, while the discriminator evaluates the outputs, and provides feedback for the generator agents to further reflect and improve the generation results. Unlike the previous generative model, our system can obtain the intermediate steps of generation. This allows each generator agent to learn from other successful executions due to its transparency, enabling a collaborative competition that enhances the quality and robustness of the system's results. The primary focus of this study is image editing, demonstrating the CCA's ability to handle intricate instructions robustly. The paper's main contributions include the introduction of a multi-agent-based generative model with controllable intermediate steps and iterative optimization, a detailed examination of agent relationships, and comprehensive experiments on image editing. Code is available at \href{https://github.com/TiankaiHang/CCA}{https://github.com/TiankaiHang/CCA}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
