11 citations · 33 across the 7 of their papers we have counts for
Showing 2023Show all
3 papers · 1 filter
cs.CV2023
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…
cs.CV2023★ 10 cited
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…
cs.CV2023★ 1 cited
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…