English(EN) Vision Models Predict Urban Scene Appraisal with Limited Neural Alignment

研究发现：视觉模型未能反映人类对城市场景的感知

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

发表在arXiv上的一项新研究挑战了视觉模型能像人类一样准确表征城市场景的假设。研究人员发现，尽管DINOv2 ViT-B等模型可以高精度地预测人类对街道场景的评估评分（最高可达r = 0.87），但它们的内部表征与人类感知脑电图（EEG）记录的神经数据并不一致。表现最好的模型仅解释了29.6%的可解释神经几何结构，并且这种一致性并未提高评估预测的准确性。这表明在评分任务中高预测准确性可能不是衡量这些模型对视觉场景理解程度的可靠指标。 AI

影响挑战了评估视觉模型对视觉场景理解能力的当前基准的可靠性。

排序理由该集群包含一篇发表在arXiv上的研究论文，详细介绍了关于AI模型表征的发现。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Kaizhen Tan, Yuantao Deng · 2026-09-01 04:00

视觉模型预测城市场景评估，神经对齐有限

arXiv:2608.30964v1 Announce Type: new Abstract: Pretrained vision embeddings are increasingly used as general-purpose representations for modelling how people appraise urban scenes, and are validated almost entirely by how well they predict human ratings. High predictive accuracy…

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视觉模型预测城市场景评估，神经对齐有限

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