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Shichao Ma

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3 papers

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AAAI Conference 2026 Conference Paper

Talk2Image: A Multi-Agent System for Multi-Turn Image Generation and Editing

Shichao Ma
Yunhe Guo
Jiahao Su
Qihe Huang
Zhengyang Zhou
Yang Wang

Text-to-image generation tasks have driven remarkable advances in diverse media applications, yet most focus on single-turn scenarios and struggle with iterative, multi-turn creative tasks. Recent dialogue-based systems attempt to bridge this gap, but their single-agent, sequential paradigm often causes intention drift and incoherent edits. To address these limitations, we present Talk2Image, a novel multi-agent system for interactive image generation and editing in multi-turn dialogue scenarios. Our approach integrates three key components: intention parsing from dialogue history, task decomposition and collaborative execution across specialized agents, and feedback-driven refinement based on a multi-view evaluation mechanism. Talk2Image enables step-by-step alignment with user intention and consistent image editing. Experiments demonstrate that Talk2Image outperforms existing baselines in controllability, coherence, and user satisfaction across iterative image generation and editing tasks.

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AAAI Conference 2025 Conference Paper

DialogDraw: Image Generation and Editing System Based on Multi-Turn Dialogue

Shichao Ma
Xinfeng Zhang
Zeng Zhao
Bai Liu
Changjie Fan
Zhipeng Hu

In recent years, diffusion modeling has shown great potential for image generation and editing. Beyond single-model approaches, various drawing workflows now exist to handle diverse drawing tasks. However, few solutions effectively identify user intentions through dialogue and progressively complete drawings. We introduce DialogDraw, which facilitates image generation and editing through continuous dialogue interaction. DialogDraw enables users to create and refine drawings using natural language and integrates with numerous open-source drawing workflows and models. The system accurately recognizes intentions and extracts user inputs via parameterization, adapts to various drawing function parameters, and provides an intuitive interaction mode. It effectively executes user instructions, supports dozens of image generation and editing methods, and offers robust scalability. Moreover, we employ SFT and RLHF to iterate the Intention Recognition and Parameter Extraction Model (IRPEM). To evaluate DialogDraw's functionality, we propose DrawnConvos, a dataset rich in drawing functions and command dialogue data collected from the open-source community. Our evaluation demonstrates that DialogDraw excels in command compliance, identifying and adapting to user drawing intentions, thereby proving the effectiveness of our method.

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EAAI Journal 2024 Journal Article

Dynamic scheduling for multi-level air defense with contingency situations based on Human-Intelligence collaboration

Rugang Tang
Xin Ning
Zheng Wang
Jiaqi Fan
Shichao Ma

Resource scheduling is an important part of military operation, especially in key-point air defense under saturation attack. Many achievements have been made in the field of radar resource scheduling and multi-aircraft scheduling. However, there is little research on the integrated scheduling of detection, tracking and attack, which will greatly increase the resource utilization to deal with the resource shortage problem caused by saturation attacks on key places. In this paper, we propose to autonomously accomplish real-time resource dispatching through end-to-end deep reinforcement learning (DRL), while allowing the commander’s intervention to accomplish a variety of complex tactics. First, an integrated scheduling model of detection, tracking and interception is proposed and transformed into a sequential decision problem by introducing a disjunctive graph and a graph neural network (GNN) to extract node features. Subsequently, the Proximal Policy Optimization (PPO) algorithm is applied to learn the air defense environment (ADE), which is modeled as an Markov decision process (MDP). Benefitting from the powerful generalization capability of the policy network, our algorithm can adapt to scheduling missions of different sizes. Moreover, we propose a novel Human-Intelligence collaborative dynamic scheduling framework for emergency response. Simulation results indicate that our algorithm generates high-quality scheduling policies for defense resources, exhibiting superior performance than existing methods. In addition, the dynamic scheduling performance of the human and intelligence collaboration approach in response to multiple contingencies is proven.

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