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arXiv · 2606.21304

A Test-time Actor-Critic Approach to News Images Generation

Damianos Galanopoulos·Vasileios Mezaris
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Abstract

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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BibTeXRIS

Damianos Galanopoulos, Vasileios Mezaris. 2026-06-19. A Test-time Actor-Critic Approach to News Images Generation. https://arxiv.org/abs/2606.21304

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