Cross-Camera Convolutional Color Constancy

Mahmoud Afifi, Jonathan T. Barron, Chloe LeGendre, Yun-Ta Tsai, Francois Bleibel; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1981-1990

Abstract


We present "Cross-Camera Convolutional Color Constancy" (C5), a learning-based method, trained on images from multiple cameras, that accurately estimates a scene's illuminant color from raw images captured by a new camera previously unseen during training. C5 is a hypernetwork-like extension of the convolutional color constancy (CCC) approach: C5 learns to generate the weights of a CCC model that is then evaluated on the input image, with the CCC weights dynamically adapted to different input content. Unlike prior cross-camera color constancy models, which are usually designed to be agnostic to the spectral properties of test-set images from unobserved cameras, C5 approaches this problem through the lens of transductive inference: additional unlabeled images are provided as input to the model at test time, which allows the model to calibrate itself to the spectral properties of the test-set camera during inference. C5 achieves state-of-the-art accuracy for cross-camera color constancy on several datasets, is fast to evaluate ( 7 and 90 ms per image on a GPU or CPU, respectively), and requires little memory ( 2 MB), and thus is a practical solution to the problem of calibration-free automatic white balance for mobile photography.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Afifi_2021_ICCV, author = {Afifi, Mahmoud and Barron, Jonathan T. and LeGendre, Chloe and Tsai, Yun-Ta and Bleibel, Francois}, title = {Cross-Camera Convolutional Color Constancy}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, month = {October}, year = {2021}, pages = {1981-1990} }