43 citations · 162 across the 34 of their papers we have counts for
7 papers · 1 filter
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…
Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)
Petru Soviany, Claudiu Ardei, Radu Tudor Ionescu +1
Despite the significant advances in recent years, Generative Adversarial Networks (GANs) are still notoriously hard to train. In this paper, we propose three novel curriculum learn…
Learning Navigation by Visual Localization and Trajectory Prediction
Iulia Paraicu, Marius Leordeanu
When driving, people make decisions based on current traffic as well as their desired route. They have a mental map of known routes and are often able to navigate without needing d…
Spacetime Graph Optimization for Video Object Segmentation
Emanuela Haller, Adina Magda Florea, Marius Leordeanu
We address the challenging task of foreground object discovery and segmentation in video. We introduce an efficient solution, suitable for both unsupervised and supervised scenario…
A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time
Elena Burceanu, Marius Leordeanu
We formulate object segmentation in video as a graph partitioning problem in space and time, in which nodes are pixels and their relations form local neighborhoods. We claim that t…
Shift R-CNN: Deep Monocular 3D Object Detection with Closed-Form Geometric Constraints
Andretti Naiden, Vlad Paunescu, Gyeongmo Kim +2
We propose Shift R-CNN, a hybrid model for monocular 3D object detection, which combines deep learning with the power of geometry. We adapt a Faster R-CNN network for regressing in…
Recurrent Space-time Graph Neural Networks
Andrei Nicolicioiu, Iulia Duta, Marius Leordeanu
Learning in the space-time domain remains a very challenging problem in machine learning and computer vision. Current computational models for understanding spatio-temporal visual…