3 citations · 4 across the 6 of their papers we have counts for
7 papers
Probabilistic Hyper-Graphs using Multiple Randomly Masked Autoencoders for Semi-supervised Multi-modal Multi-task Learning
Pîrvu Mihai-Cristian, Marius Leordeanu
The computer vision domain has greatly benefited from an abundance of data across many modalities to improve on various visual tasks. Recently, there has been a lot of focus on sel…
Multi-modal video data-pipelines for machine learning with minimal human supervision
Mihai-Cristian Pîrvu, Marius Leordeanu
The real-world is inherently multi-modal at its core. Our tools observe and take snapshots of it, in digital form, such as videos or sounds, however much of it is lost. Similarly f…
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…
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…
Pose2RGBD. Generating Depth and RGB images from absolute positions
Mihai Cristian Pîrvu
We propose a method at the intersection of Computer Vision and Computer Graphics fields, which automatically generates RGBD images using neural networks, based on previously seen a…