Afifi et al. proposes a learning-based colour constancy method Cross-Camera Convolutional Color Constancy (C5), which is trained on images from multiple cameras. The proposed method 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 unlabelled images are provided as provided to the model at test time, which allows the model to calibrate itself to the spectral properties of the test-set camera during inference.