超越度量偏差的遥感图像描述评估
Evaluating Remote Sensing Image Captions Beyond Metric Biases
AI总结：
本文提出ReconScore指标，通过文本重建能力评估描述质量，发现未微调的MLLM在零样本遥感图像描述任务中表现更优，并引入无需训练的RemoteDescriber方法提升语义精度。
摘要：
图像描述的核心目标是实现从视觉信号到文本模态的无损语义压缩。然而，依赖人工编纂的参考文本进行评估迫使模型模仿特定的人类注释风格，掩盖了先进基础模型的真实描述能力。本文提出ReconScore，一种无需参考的评估度量，通过文本重建能力评估描述质量，揭示未微调的MLLM在零样本遥感图像描述任务中表现更优。基于此发现，本文引入RemoteDescriber，一种完全无需训练的生成方法，通过ReconScore作为自修正机制迭代优化MLLM输出的语义精度。实验表明，RemoteDescriber在三个数据集上达到最先进的性能，并验证了ReconScore的可靠性，分析了传统度量的缺陷。代码可在https://github.com/hhu-czy/RemoteDescriber获取。
英文摘要：
The core objective of image captioning is to achieve lossless semantic compression from visual signals into textual modalities. However, the reliance on manually curated reference texts for evaluation essentially forces models to mimic specific human annotation styles, thereby masking the true descriptive capabilities of advanced foundation models. This systemic misalignment prompts a critical question: Is task-specific fine-tuning truly necessary for Remote Sensing Image Captioning, or is the perceived performance gap merely an artifact of flawed evaluation criteria? To investigate this discrepancy, we propose ReconScore, a novel reference-free evaluation metric. Rather than computing textual similarities, we assess caption quality by its capability to reconstruct the original visual elements solely from the generated text, effectively neutralizing human annotation biases. Applying this metric, we uncover a profound, counterintuitive truth: inherently powerful, unfine-tuned MLLMs surpass their fine-tuned counterparts in authentic zero-shot RSIC tasks. Driven by this structural discovery, we introduce RemoteDescriber, a completely training-free generation methodology. By employing ReconScore as a self-correction mechanism, we iteratively refine the semantic precision of MLLM outputs without any computational fine-tuning overhead. Comprehensive experiments demonstrate that RemoteDescriber achieves state-of-the-art performance on three datasets. Furthermore, we validate ReconScore's reliability and analyze the flaws of traditional metrics. Our code is available at https://github.com/hhu-czy/RemoteDescriber.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
