CCA: Collaborative Competitive Agents for Image Editing

• URL: http://arxiv.org/abs/2401.13011v2
• Date: Sat, 15 Feb 2025 13:26:28 GMT
• Title: CCA: Collaborative Competitive Agents for Image Editing
• Authors: Tiankai Hang, Shuyang Gu, Dong Chen, Xin Geng, Baining Guo,
• Abstract summary: This paper presents a novel generative model, Collaborative Competitive Agents (CCA) It leverages the capabilities of multiple Large Language Models (LLMs) based agents to execute complex tasks. The paper's main contributions include the introduction of a multi-agent-based generative model with controllable intermediate steps and iterative optimization.
• Score: 55.500493143796405
• License:
• Abstract: 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}.

Related papers

• GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration [20.988801611785522]
  We propose GenMAC, an iterative, multi-agent framework that enables compositional text-to-video generation. The collaborative workflow includes three stages: Design, Generation, and Redesign. To tackle diverse scenarios of compositional text-to-video generation, we design a self-routing mechanism to adaptively select the proper correction agent from a collection of correction agents each specialized for one scenario.
  arXiv  Detail & Related papers  (2024-12-05T18:56:05Z)
• Gödel Agent: A Self-Referential Agent Framework for Recursive Self-Improvement [117.94654815220404]
  G"odel Agent is a self-evolving framework inspired by the G"odel machine. G"odel Agent can achieve continuous self-improvement, surpassing manually crafted agents in performance, efficiency, and generalizability.
  arXiv  Detail & Related papers  (2024-10-06T10:49:40Z)
• Textualized Agent-Style Reasoning for Complex Tasks by Multiple Round LLM Generation [49.27250832754313]
  We present AgentCOT, a llm-based autonomous agent framework. At each step, AgentCOT selects an action and executes it to yield an intermediate result with supporting evidence. We introduce two new strategies to enhance the performance of AgentCOT.
  arXiv  Detail & Related papers  (2024-09-19T02:20:06Z)
• Scaling Large-Language-Model-based Multi-Agent Collaboration [75.5241464256688]
  Pioneering advancements in large language model-powered agents have underscored the design pattern of multi-agent collaboration. Inspired by the neural scaling law, this study investigates whether a similar principle applies to increasing agents in multi-agent collaboration.
  arXiv  Detail & Related papers  (2024-06-11T11:02:04Z)
• Concept Matching with Agent for Out-of-Distribution Detection [19.407364109506904]
  We propose a new method that integrates the agent paradigm into out-of-distribution (OOD) detection task. Our proposed method, Concept Matching with Agent (CMA), employs neutral prompts as agents to augment the CLIP-based OOD detection process. Our extensive experimental results showcase the superior performance of CMA over both zero-shot and training-required methods.
  arXiv  Detail & Related papers  (2024-05-27T02:27:28Z)
• Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation [72.6168579583414]
  CompAgent is a training-free approach for compositional text-to-image generation with a large language model (LLM) agent as its core. Our approach achieves more than 10% improvement on T2I-CompBench, a comprehensive benchmark for open-world compositional T2I generation.
  arXiv  Detail & Related papers  (2024-01-28T16:18:39Z)
• Graph Convolutional Value Decomposition in Multi-Agent Reinforcement Learning [9.774412108791218]
  We propose a novel framework for value function factorization in deep reinforcement learning. In particular, we consider the team of agents as the set of nodes of a complete directed graph. We introduce a mixing GNN module, which is responsible for i) factorizing the team state-action value function into individual per-agent observation-action value functions, and ii) explicit credit assignment to each agent in terms of fractions of the global team reward.
  arXiv  Detail & Related papers  (2020-10-09T18:01:01Z)
• Multi-Agent Interactions Modeling with Correlated Policies [53.38338964628494]
  In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework. We develop a Decentralized Adrial Imitation Learning algorithm with Correlated policies (CoDAIL) Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators.
  arXiv  Detail & Related papers  (2020-01-04T17:31:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.