English(EN) How Murky Before a VLM Knows It Cannot See? Accuracy, Confidence and Abstention on a Physical Visibility Axis

视觉语言模型在能见度下降时仍表现出高置信度，尽管准确性有所下降

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-19 04:00

一篇新发表在arXiv上的研究论文，通过在能见度下降的条件下测试视觉语言模型（VLMs）的性能，来探究其鲁棒性。研究人员使用以米为单位的物理可见性轴，测量了模型在面对雾和水下环境时的准确性、置信度以及模型弃权的能力。研究结果表明，即使准确性显著下降，VLMs通常仍能保持高置信度，这揭示了它们感知能力中存在的重大盲点。 AI

影响突显了VLM可靠性方面的一个关键差距，表明当前模型可能夸大了它们在真实世界、能见度下降条件下的理解能力。

排序理由该集群包含一篇详细介绍VLM鲁棒性新研究发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Mochamad Rizal Fauzan, Irgi Surya · 2026-10-19 04:00

视觉语言模型（VLM）在知道自己无法看见之前有多模糊？物理可见性轴上的准确性、置信度和弃权

arXiv:2610.22981v1 Announce Type: cross Abstract: Robustness studies of vision-language models (VLMs) grade degradation on nominal severity scales ("level 1-5") that carry no physical units, and they rarely ask whether a model's confidence follows the loss of visibility. We measu…

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报道来源 [1]

视觉语言模型（VLM）在知道自己无法看见之前有多模糊？物理可见性轴上的准确性、置信度和弃权

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