Search Results for author:
Found 9 papers, 7 papers with code
UnrealROX+: An Improved Tool for Acquiring Synthetic Data from Virtual 3D Environments
Synthetic data generation has become essential in last years for feeding data-driven algorithms, which surpassed traditional techniques performance in almost every computer vision problem.
H-GAN: the power of GANs in your Hands
Relying on image-to-image translation, we improve the appearance of synthetic hands to approximate the statistical distribution underlying a collection of real images of hands.
A Review on Deep Learning Techniques for Video Prediction
The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems.
UA at SemEval-2019 Task 5: Setting A Strong Linear Baseline for Hate Speech Detection
This paper describes the system developed at the University of Alicante (UA) for the SemEval 2019 Task 5: Shared Task on Multilingual Detection of Hate.
A Visually Plausible Grasping System for Object Manipulation and Interaction in Virtual Reality Environments
On the other hand, for the quantitative evaluation a novel error metric has been proposed to visually analyze the performed grips.
The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions
Enter the RobotriX, an extremely photorealistic indoor dataset designed to enable the application of deep learning techniques to a wide variety of robotic vision problems.
TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors
Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp.
UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation
Gathering and annotating that sheer amount of data in the real world is a time-consuming and error-prone task.
A Review on Deep Learning Techniques Applied to Semantic Segmentation
This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding.
