43 citations · 162 across the 34 of their papers we have counts for
6 papers · 1 filter
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…
JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition
Lucian Bicsi, Bogdan Alexe, Radu Tudor Ionescu +1
We propose JEDI, a multi-dataset semi-supervised learning method, which efficiently combines knowledge from multiple experts, learned on different datasets, to train and improve th…
Self-supervised novel 2D view synthesis of large-scale scenes with efficient multi-scale voxel carving
Alexandra Budisteanu, Dragos Costea, Alina Marcu +1
The task of generating novel views of real scenes is increasingly important nowadays when AI models become able to create realistic new worlds. In many practical applications, it i…
GEST: the Graph of Events in Space and Time as a Common Representation between Vision and Language
Mihai Masala, Nicolae Cudlenco, Traian Rebedea +1
One of the essential human skills is the ability to seamlessly build an inner representation of the world. By exploiting this representation, humans are capable of easily finding c…
Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms
Florin Condrea, Saikiran Rapaka, Lucian Itu +4
Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, through computed tomographic pulmonary angiography (CTPA), represents the gold st…