10 citations · 22 across the 5 of their papers we have counts for
5 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…
Hybrid Open-set Segmentation with Synthetic Negative Data
Matej Grcić, Siniša Šegvić
Open-set segmentation can be conceived by complementing closed-set classification with anomaly detection. Many of the existing dense anomaly detectors operate through generative mo…
On Advantages of Mask-level Recognition for Outlier-aware Segmentation
Matej Grcić, Josip Šarić, Siniša Šegvić
Most dense recognition approaches bring a separate decision in each particular pixel. These approaches deliver competitive performance in usual closed-set setups. However, importan…
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…
Dense Out-of-Distribution Detection by Robust Learning on Synthetic Negative Data
Matej Grcić, Petra Bevandić, Zoran Kalafatić +1
Standard machine learning is unable to accommodate inputs which do not belong to the training distribution. The resulting models often give rise to confident incorrect predictions…