English(EN) You Cannot Photograph the Same Street Twice: Reliability Limits in Vision-Language Measurement of Urban Change

视觉-语言模型在测量城市变迁方面可靠性差

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

arXiv上发表的一项新研究强调，在使用视觉-语言模型从街景图像测量城市变迁时存在显著的可靠性问题。研究人员发现，重新拍摄同一条街道会将感知分数平均改变0.80分，这一变化与两条不同街道之间的差异相当。虽然重复调用模型对这种变化的贡献很小，但图像重新编码和提示顺序等因素对分数有显著影响。即使是微小的物理变化或采集条件的变化，也会导致模型在相同的场景中报告物理变化，尽管聚合数百次观测可以恢复连贯的再开发信号。 AI

影响强调了在城市监测等实际应用中，视觉-语言模型需要提高鲁棒性和验证性。

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arXiv cs.CV TIER_1 English(EN) · Kaizhen Tan · 2026-09-02 04:00

同一条街道无法拍摄两次：城市变迁的视觉语言测量中的可靠性限制

arXiv:2609.00649v1 Announce Type: new Abstract: Vision-language models are increasingly used to measure urban change from repeated street-level imagery, but their longitudinal reliability is not well understood. We test how much a perception score can change when the street itsel…

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同一条街道无法拍摄两次：城市变迁的视觉语言测量中的可靠性限制

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