44 citations · 122 across the 16 of their papers we have counts for
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cs.CV2022★ 6 cited
Weakly supervised training of universal visual concepts for multi-domain semantic segmentation
Petra Bevandić, Marin Oršić, Ivan Grubišić +2
Deep supervised models have an unprecedented capacity to absorb large quantities of training data. Hence, training on multiple datasets becomes a method of choice towards strong ge…
cs.CV2022★ 1 cited
Automatic universal taxonomies for multi-domain semantic segmentation
Petra Bevandić, Siniša Šegvić
Training semantic segmentation models on multiple datasets has sparked a lot of recent interest in the computer vision community. This interest has been motivated by expensive anno…
cs.CV2022
Panoptic SwiftNet: Pyramidal Fusion for Real-time Panoptic Segmentation
Josip Šarić, Marin Oršić, Siniša Šegvić
Dense panoptic prediction is a key ingredient in many existing applications such as autonomous driving, automated warehouses or remote sensing. Many of these applications require f…