Regressing the illumination of a scene from the representations of object
appearances is popularly adopted in computational color constancy. However,
it's still challenging due to intrinsic appearance and label ambiguities caused
by unknown illuminants, diverse reflection property of materials and extrinsic
imaging factors (such as different camera sensors). In this paper, we introduce
a novel algorithm by Cascading Convolutional Color Constancy (in short, C4) to
improve robustness of regression learning and achieve stable generalization
capability across datasets (different cameras and scenes) in a unique
framework. The proposed C4 method ensembles a series of dependent illumination
hypotheses from each cascade stage via introducing a weighted
multiply-accumulate loss function, which can inherently capture different modes
of illuminations and explicitly enforce coarse-to-fine network optimization.
Experimental results on the public Color Checker and NUS 8-Camera benchmarks
demonstrate superior performance of the proposed algorithm in comparison with
the state-of-the-art methods, especially for more difficult scenes.

本文提出了一种基于加权乘累积损失函数的级联卷积色彩恒常性算法 (C4)，可以改善回归学习的鲁棒性，并在独特的框架下实现数据集 (不同相机和场景) 的稳定泛化能力。与最先进的方法相比，实验结果表明，该算法在公共 Color Checker 和 NUS 8-Camera 基准测试中表现出卓越的性能，特别是对于较难的场景。