4 accepted papers
This paper presents a novel approach to forest habitat monitoring using robotics and advanced data analysis techniques. We introduce a quadrupedal robot with LiDAR and onboard cameras to collect detailed data about forest structure and composition. The data is then processed using a combination of d…
In this work, we pose the question whether it is possible to design and train an autoencoder model in an end-to-end fashion to learn representations in the multivariate Bernoulli latent space, and achieve performance comparable with the state-of-the-art variational methods. Moreover, we investigate…
Deep Neural networks (DNN) dominate the state of art results in computer vision (CV) and other fields. One of the primary reasons why DNN outperform existing algorithms is that these produce superior results when more labelled data are used, unlike classic CV techniques. Nonetheless, it is well know…
In this paper we present the design and evaluation of an end to end trainable, deep neural network with a visual attention mechanism for memorability estimation in still images. We analyze the suitability of transfer learning of deep models from image classification to the memorability task. Further…