3 citations · 3 across the 4 of their papers we have counts for
7 papers
Multi-Task Hypergraphs for Semi-supervised Learning using Earth Observations
Mihai Pirvu, Alina Marcu, Alexandra Dobrescu +2
There are many ways of interpreting the world and they are highly interdependent. We exploit such complex dependencies and introduce a powerful multi-task hypergraph, in which ever…
Self-supervised Hypergraphs for Learning Multiple World Interpretations
Alina Marcu, Mihai Pirvu, Dragos Costea +5
We present a method for learning multiple scene representations given a small labeled set, by exploiting the relationships between such representations in the form of a multi-task…
Semantics through Time: Semi-supervised Segmentation of Aerial Videos with Iterative Label Propagation
Alina Marcu, Vlad Licaret, Dragos Costea +1
Semantic segmentation is a crucial task for robot navigation and safety. However, current supervised methods require a large amount of pixelwise annotations to yield accurate resul…
Semi-Supervised Learning for Multi-Task Scene Understanding by Neural Graph Consensus
Marius Leordeanu, Mihai Pirvu, Dragos Costea +3
We address the challenging problem of semi-supervised learning in the context of multiple visual interpretations of the world by finding consensus in a graph of neural networks. Ea…
Towards Automatic Annotation for Semantic Segmentation in Drone Videos
Alina Marcu, Dragos Costea, Vlad Licaret +1
Semantic segmentation is a crucial task for robot navigation and safety. However, it requires huge amounts of pixelwise annotations to yield accurate results. While recent progress…
A Multi-Stage Multi-Task Neural Network for Aerial Scene Interpretation and Geolocalization
Alina Marcu, Dragos Costea, Emil Slusanschi +1
Semantic segmentation and vision-based geolocalization in aerial images are challenging tasks in computer vision. Due to the advent of deep convolutional nets and the availability…