Estibaliz Garrote

TL;DR: This paper uses the largest action video database to-date with 51 action categories, which in total contain around 7,000 manually annotated clips extracted from a variety of sources ranging from digitized movies to YouTube, to evaluate the performance of two representative computer vision systems for action recognition and explore the robustness of these methods under various conditions.

TL;DR: A trainable computer vision system enabling the automated analysis of complex mouse behaviours that performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice.

TL;DR: This work applies the emph{shades of gray} color constancy technique to color-normalize the entire training set of images, while retaining the estimated illuminants, for training two deep convolutional neural networks for the tasks of skin lesion segmentation and skin lesions classification.

TL;DR: A deep learning architecture based on 1D-CNN layers and a Long Short-Term Memory (LSTM) network for the detection of VF is introduced, believed to be the most accurate VF detection algorithm to date, especially on OHCA data, and it would enable an accurate shock no shock diagnosis in a very short time.

TL;DR: In this article, a conditional generative adversarial framework is used to capture spatial semantics for hyperspectral image reconstruction, achieving a Root Mean Squared Error (RMSE) drop of 44.7% and a Relative RMSE drop of 47.0%.