用深度模型模拟人类颜色恒常性，验证其与人眼表现高度一致。
Human-Aligned Evaluation of a Pixel-wise DNN Color Constancy Model
- 基于残差U-Net的模型通过迁移学习适应不同光照条件。
- 模型与人类在基础条件下均实现高颜色恒常性，退化时表现相似。
- 首次以人眼任务为基准评估神经网络颜色恒常性，更具生态效度。
我们此前研究了虚拟现实（VR）中的颜色恒常性问题，并开发了一种从渲染图像中预测表面反射率的深度神经网络（DNN）。本文结合两种方法，对比分析该模型与人类在经典颜色恒常性机制（局部周边、最大通量、空间均值）下的表现。不同于使用物理真值评估，模型性能通过与人类实验相同的无色物体选择任务进行评价。模型采用先前工作中的基于ResNet的U-Net结构，在渲染图像上预训练以预测反射率，随后仅对解码器部分进行微调，适配基线VR条件下的图像。为匹配人类实验流程，模型输出用于执行相同无色物体选择任务。结果表明，模型与人类行为表现出强一致性：在基线条件下两者均达到高颜色恒常性，且当移除局部周边或空间均值颜色线索时，二者均出现类似程度的性能下降。
原文摘要 · Abstract (English)
We previously investigated color constancy in photorealistic virtual reality (VR) and developed a Deep Neural Network (DNN) that predicts reflectance from rendered images. Here, we combine both approaches to compare and study a model and human performance with respect to established color constancy mechanisms: local surround, maximum flux and spatial mean. Rather than evaluating the model against physical ground truth, model performance was assessed using the same achromatic object selection task employed in the human experiments. The model, a ResNet based U-Net from our previous work, was pre-trained on rendered images to predict surface reflectance. We then applied transfer learning, fine-tuning only the network's decoder on images from the baseline VR condition. To parallel the human experiment, the model's output was used to perform the same achromatic object selection task across all conditions. Results show a strong correspondence between the model and human behavior. Both achieved high constancy under baseline conditions and showed similar, condition-dependent performance declines when the local surround or spatial mean color cues were removed.
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