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REVEALER: Reinforcement-Guided Visual Reasoning for Element-Level Text-Image Alignment Evaluation
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
Evaluating the alignment between textual prompts and generated images is critical for ensuring the reliability and usability of text-to-image (T2I) models. However, most existing evaluation methods rely on coarse-grained metrics or static Question Answering (QA) pipelines, which lack fine-grained interpretability and struggle to reflect human preferences. To address this, we propose \textbf{REVEALER}, a reinforcement-guided visual reasoning framework for element-level text-to-image alignment evaluation. Adopting a structured ''grounding–reasoning–conclusion'' paradigm, our method enables Multimodal Large Language Models (MLLMs) to explicitly localize semantic elements and derive interpretable alignment judgments. We optimize the model via Group Relative Policy Optimization (GRPO) using a multi-dimensional reward function that targets format compliance, localization precision, and alignment accuracy.Extensive experiments confirm that REVEALER achieves state-of-the-art results across four benchmarks. Notably, on EvalMuse-40K, it surpasses the strong proprietary Gemini 3 Pro and Training-based baselines with absolute accuracy gains of \textbf{+4.2\%} and \textbf{+13.3\%}, respectively. Ablation studies further demonstrate the efficacy of our method, contributing a cumulative \textbf{19.6\%} improvement over the base model.
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记录 #1 · 2026-07 · 最新观测版本
REVEALER: Reinforcement-Guided Visual Reasoning for Element-Level Text-Image Alignment Evaluation
Evaluating the alignment between textual prompts and generated images is critical for ensuring the reliability and usability of text-to-image (T2I) models. However, most existing evaluation methods rely on coarse-grained metrics or static Question Answering (QA) pipelines, which lack fine-grained interpretability and struggle to reflect human preferences. To address this, we propose \textbf{REVEALER}, a reinforcement-guided visual reasoning framework for element-level text-to-image alignment evaluation. Adopting a structured ''grounding–reasoning–conclusion'' paradigm, our method enables Multimodal Large Language Models (MLLMs) to explicitly localize semantic elements and derive interpretable alignment judgments. We optimize the model via Group Relative Policy Optimization (GRPO) using a multi-dimensional reward function that targets format compliance, localization precision, and alignment accuracy.Extensive experiments confirm that REVEALER achieves state-of-the-art results across four benchmarks. Notably, on EvalMuse-40K, it surpasses the strong proprietary Gemini 3 Pro and Training-based baselines with absolute accuracy gains of \textbf{+4.2\%} and \textbf{+13.3\%}, respectively. Ablation studies further demonstrate the efficacy of our method, contributing a cumulative \textbf{19.6\%} improvement over the base model.
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