基于强化学习反馈机制，动态优化新闻图像生成提示。
A Test-time Actor-Critic Approach to News Images Generation
- 测试时用自适应提示迭代生成与评估图像。
- 在2026媒体评测中表现最佳，超越现有方法。
- 适用于需要高相关性图像生成的新闻场景。
本文提出CERTH-ITI团队参与MediaEval NewsImages 2026挑战的解决方案，聚焦于根据新闻标题生成相关图像。受强化学习中行为者-评论者范式的启发，我们提出一种测试时、模型无关的行为者-评论者图像生成方法（ACIG）。ACIG通过生成图像生成提示，生成图像，评估结果，并在必要时基于反馈循环优化提示，实现闭环改进。该方法在新闻图像生成任务中表现优异，获得挑战赛排行榜第一的成绩。
原文摘要 · Abstract (English)
This paper introduces the CERTH-ITI solution for the MediaEval NewsImages 2026 challenge, which focuses on generating images related to news headlines. Inspired by the Actor-Critic paradigm in reinforcement learning, we present a test-time, model-agnostic Actor-Critic Image Generation approach (ACIG). ACIG generates prompts for image creation, produces the images, evaluates the generated results, and if needed refines the image generation prompts accordingly in a feedback loop. ACIG achieved the best results in the NewsImages 2026 challenge, according to the challenge's leaderboard.
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