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We introduce a novel formulation of temporal color constancy which considers multiple frames preceding the frame for which illumination is estimated. We propose an end-to-end trainable recurrent color constancy network – the RCC-Net – which exploits convolutional LSTMs and a simulated sequence to learn compositional representations in space and time. We use a standard single frame color constancy...
A Look-Up Table (LUT) shows modest performance (delay and power) when used as a universal logic module (ULM) for implementing all possible combinational functions; moreover, the complete programmability of a LUT (so for all functions) incurs in a significant circuit complexity. Few approaches have been proposed by which a LUT is replaced by circuits; this is possible because in practice, the number...
The color constancy problem is addressed by structured-output regression on the values of the fully-connected layers of a convolutional neural network. The AlexNet and the VGG are considered and VGG slightly outperformed AlexNet. Best results were obtained with the first fully-connected “fc6” layer and with multi-output support vector regression. Experiments on the SFU Color Checker and Indoor Dataset...
Error back-propagation is one of the principled learning strategies widely used in pattern recognition and machine learning, e.g. neural networks. The existing frameworks employed back-propagated error as a performance criteria (or termed, object function) aiming for supervising model-learning. Inspired by the recent success achieved by learning with the privileged information (LPI), we propose a...
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