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SDAT: Sub-Dataset Alternation Training for Improved Image Demosaicing
In data centric approaches, such as deep learning, the distribution of the dataset used for training can impose a bias on the networks' outcome.
Do More With What You Have: Transferring Depth-Scale from Labeled to Unlabeled Domains
We use this observed property to transfer the depth-scale from source datasets that have absolute depth labels to new target datasets that lack these measurements, enabling absolute depth predictions in the target domain.
You Better Look Twice: a new perspective for designing accurate detectors with reduced computations
This method can be applied on other object detection applications in scenes with a considerable amount of background and variate object sizes to reduce computations.
IM-Net for High Resolution Video Frame Interpolation
In this paper we propose IM-Net: an interpolated motion neural network.
ANALYSIS OF CALIBRATED SEA CLUTTER AND BOAT REFLECTIVITY DATA AT C- AND X-BAND IN SOUTH AFRICAN COASTAL WATERS
Abstract—The synthesis-based sparse representation model for signals has drawn considerable interest in the past decade.
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