4 citations · 5 across the 4 of their papers we have counts for
4 papers
Real time dense anomaly detection by learning on synthetic negative data
Anja Delić, Matej Grcić, Siniša Šegvić
Most approaches to dense anomaly detection rely on generative modeling or on discriminative methods that train with negative data. We consider a recent hybrid method that optimizes…
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…
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition
Matej Grcić, Petra Bevandić, Siniša Šegvić
Anomaly detection can be conceived either through generative modelling of regular training data or by discriminating with respect to negative training data. These two approaches ex…
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…