3 papers
cs.CV2021
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…
cs.CV2020
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…
cs.CV2018
Dynamic Adaptation on Non-Stationary Visual Domains
Sindi Shkodrani, Michael Hofmann, Efstratios Gavves
Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of statio…