2 citations · 4 across the 4 of their papers we have counts for
5 papers
Detecting Objects with Context-Likelihood Graphs and Graph Refinement
Aritra Bhowmik, Yu Wang, Nora Baka +2
The goal of this paper is to detect objects by exploiting their interrelationships. Contrary to existing methods, which learn objects and relations separately, our key idea is to l…
United We Learn Better: Harvesting Learning Improvements From Class Hierarchies Across Tasks
Sindi Shkodrani, Yu Wang, Marco Manfredi +1
Attempts of learning from hierarchical taxonomies in computer vision have been mostly focusing on image classification. Though ways of best harvesting learning improvements from hi…
Prior to Segment: Foreground Cues for Weakly Annotated Classes in Partially Supervised Instance Segmentation
David Biertimpel, Sindi Shkodrani, Anil S. Baslamisli +1
Instance segmentation methods require large datasets with expensive and thus limited instance-level mask labels. Partially supervised instance segmentation aims to improve mask pre…
Exploiting Temporality for Semi-Supervised Video Segmentation
Radu Sibechi, Olaf Booij, Nora Baka +1
In recent years, there has been remarkable progress in supervised image segmentation. Video segmentation is less explored, despite the temporal dimension being highly informative.…
EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection
Mohsen Ghafoorian, Cedric Nugteren, Nóra Baka +2
Convolutional neural networks have been successfully applied to semantic segmentation problems. However, there are many problems that are inherently not pixel-wise classification p…