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Found 5 papers, 1 papers with code
The Role of the Input in Natural Language Video Description
Natural Language Video Description (NLVD) has recently received strong interest in the Computer Vision, Natural Language Processing (NLP), Multimedia, and Autonomous Robotics communities.
Towards Monocular Digital Elevation Model (DEM) Estimation by Convolutional Neural Networks - Application on Synthetic Aperture Radar Images
In this work, the authors propose a novel experimental alternative to the InSAR method that uses single-pass acquisitions, using a data driven approach implemented by Deep Neural Networks.
J-MOD$^{2}$: Joint Monocular Obstacle Detection and Depth Estimation
In this work, we propose an end-to-end deep architecture that jointly learns to detect obstacles and estimate their depth for MAV flight applications.
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation
In addition, we propose to learn the latent space jointly with the estimation task, so that the learned OF features become a more robust description of the OF input.
Fast Robust Monocular Depth Estimation for Obstacle Detection with Fully Convolutional Networks
We propose a novel appearance-based Object Detection system that is able to detect obstacles at very long range and at a very high speed (~300Hz), without making assumptions on the type of motion.
