Sensor Distance Learning For Cross-Camera Color Constancy

Computational color constancy has seen strong improvement these last years due to the emergence of large labeled datasets. However, the models trained on images acquired by some cameras show low generalization power when tested on images acquired by other cameras. Indeed, since the light chromaticities are device dependent, the training distribution is very spread out when mixing different sensors. In this paper, we propose to inform the network that this complex distribution is a set of simpler distributions, one for each considered camera. For this purpose, we create a Siamese architecture trained with a specific contrastive loss. This loss enforces the model to predict light chromaticities in the same sensor distribution, when considering images acquired by the same camera and in different distributions for images from different sensors. The key idea consists in learning a specific color distance that is sensitive to only sensor variations and not to lighting variations. This learned distance is a nice tool to control if two chromaticity points are in the same sensor distribution or not. We test this original training process in the context of cross-camera color constancy and we show that it outperforms the alternatives on three datasets.