7 citations · 17 across the 10 of their papers we have counts for
14 papers
MC-PanDA++: Simpler, Stronger, and More Robust Domain-Adaptive Panoptic Segmentation
Ivan Martinović, Josip Šarić, Yuki M. Asano +1
Unsupervised domain adaptation (UDA) reduces the annotation burden in panoptic segmentation by leveraging a cost-effectively labeled source domain (e.g., synthetic) and an unlabele…
BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks
Ivan Sabolić, Marin Oršić, Josip Šarić +1
Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks. Recent work has shown that this paradigm is highly vulner…
4th Workshop on Maritime Computer Vision (MaCVi): Challenge Overview
Benjamin Kiefer, Jan Lukas Augustin, Jon Muhovič +52
The 4th Workshop on Maritime Computer Vision (MaCVi) is organized as part of CVPR 2026. This edition features five benchmark challenges with emphasis on both predictive accuracy an…
Mitigating Objectness Bias and Region-to-Text Misalignment for Open-Vocabulary Panoptic Segmentation
Nikolay Kormushev, Josip Šarić, Matej Kristan
Open-vocabulary panoptic segmentation remains hindered by two coupled issues: (i) mask selection bias, where objectness heads trained on closed vocabularies suppress masks of categ…
What Holds Back Open-Vocabulary Segmentation?
Josip Šarić, Ivan Martinović, Matej Kristan +1
Standard segmentation setups are unable to deliver models that can recognize concepts outside the training taxonomy. Open-vocabulary approaches promise to close this gap through la…
DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic Segmentation
Ivan Martinović, Josip Šarić, Marin Oršić +2
Pixel-level annotation is expensive and time-consuming. Semi-supervised segmentation methods address this challenge by learning models on few labeled images alongside a large corpu…
MC-PanDA: Mask Confidence for Panoptic Domain Adaptation
Ivan Martinović, Josip Šarić, Siniša Šegvić
Domain adaptive panoptic segmentation promises to resolve the long tail of corner cases in natural scene understanding. Previous state of the art addresses this problem with cross-…
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…
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…
Dense Semantic Forecasting in Video by Joint Regression of Features and Feature Motion
Josip Šarić, Sacha Vražić, Siniša Šegvić
Dense semantic forecasting anticipates future events in video by inferring pixel-level semantics of an unobserved future image. We present a novel approach that is applicable to va…
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…
Multi-domain semantic segmentation with pyramidal fusion
Petra Bevandić, Marin Oršić, Ivan Grubišić +2
We present our submission to the semantic segmentation contest of the Robust Vision Challenge held at ECCV 2020. The contest requires submitting the same model to seven benchmarks…
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…