11 citations · 11 across the 1 of their papers we have counts for
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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…
Dense open-set recognition with synthetic outliers generated by Real NVP
Matej Grcić, Petra Bevandić, Siniša Šegvić
Today's deep models are often unable to detect inputs which do not belong to the training distribution. This gives rise to confident incorrect predictions which could lead to devas…
Multimodal semantic forecasting based on conditional generation of future features
Kristijan Fugošić, Josip Šarić, Siniša Šegvić
This paper considers semantic forecasting in road-driving scenes. Most existing approaches address this problem as deterministic regression of future features or future predictions…
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain Shift
Petra Bevandić, Ivan Krešo, Marin Oršić +1
Recent success on realistic road driving datasets has increased interest in exploring robust performance in real-world applications. One of the major unsolved problems is to identi…
Single Level Feature-to-Feature Forecasting with Deformable Convolutions
Josip Šarić, Marin Oršić, Tonći Antunović +2
Future anticipation is of vital importance in autonomous driving and other decision-making systems. We present a method to anticipate semantic segmentation of future frames in driv…
Pedestrian Tracking by Probabilistic Data Association and Correspondence Embeddings
Borna Bićanić, Marin Oršić, Ivan Marković +2
This paper studies the interplay between kinematics (position and velocity) and appearance cues for establishing correspondences in multi-target pedestrian tracking. We investigate…