论文标题
MIMT：通过多任务局部表面和浅色学习的多弹性颜色恒定
MIMT: Multi-Illuminant Color Constancy via Multi-Task Local Surface and Light Color Learning
论文作者
论文摘要
均匀的浅色分布的假设不再适用于具有多种浅色的场景。大多数颜色恒定方法旨在处理单个浅色，因此当应用于多种浅色时是错误的。多种浅色的空间变异性会导致颜色恒定问题更具挑战性，并且需要提取本地表面/光信息。在此激励的情况下，我们引入了一种多任务学习方法，以在单个输入图像中打折多种浅色。为了更好地提示在多种浅色条件下，我们设计了一个新颖的多任务学习框架。我们的框架包括辅助像素检测和表面色相似性预测的辅助任务，分别为局部光和表面颜色提供了更好的提示。此外，为确保我们的模型保持表面颜色的恒定，无论浅色的变化如何，都开发了一种新型的本地表面颜色保存方案。我们证明，与在多弹药数据集（LSMI）上的最先进的多弹药颜色恒定方法相比，我们的模型可实现47.1％的改善（从4.69平均角误差到2.48）。
The assumption of a uniform light color distribution is no longer applicable in scenes that have multiple light colors. Most color constancy methods are designed to deal with a single light color, and thus are erroneous when applied to multiple light colors. The spatial variability in multiple light colors causes the color constancy problem to be more challenging and requires the extraction of local surface/light information. Motivated by this, we introduce a multi-task learning method to discount multiple light colors in a single input image. To have better cues of the local surface/light colors under multiple light color conditions, we design a novel multi-task learning framework. Our framework includes auxiliary tasks of achromatic-pixel detection and surface-color similarity prediction, providing better cues for local light and surface colors, respectively. Moreover, to ensure that our model maintains the constancy of surface colors regardless of the variations of light colors, a novel local surface color feature preservation scheme is developed. We demonstrate that our model achieves 47.1% improvement (from 4.69 mean angular error to 2.48) compared to a state-of-the-art multi-illuminant color constancy method on a multi-illuminant dataset (LSMI).
