44 citations · 122 across the 16 of their papers we have counts for
24 papers
Identifying Label Errors in Object Detection Datasets by Loss Inspection
Marius Schubert, Tobias Riedlinger, Karsten Kahl +4
Labeling datasets for supervised object detection is a dull and time-consuming task. Errors can be easily introduced during annotation and overlooked during review, yielding inaccu…
Normalizing Flow based Feature Synthesis for Outlier-Aware Object Detection
Nishant Kumar, Siniša Šegvić, Abouzar Eslami +1
Real-world deployment of reliable object detectors is crucial for applications such as autonomous driving. However, general-purpose object detectors like Faster R-CNN are prone to…
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…
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…
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…
Multi-domain semantic segmentation with overlapping labels
Petra Bevandić, Marin Oršić, Ivan Grubišić +2
Deep supervised models have an unprecedented capacity to absorb large quantities of training data. Hence, training on many datasets becomes a method of choice towards graceful degr…