Josip Saric

Orcid: 0000-0001-7262-550X

According to our database1, Josip Saric authored at least 14 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

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Bibliography

2024
Weakly Supervised Training of Universal Visual Concepts for Multi-domain Semantic Segmentation.
Int. J. Comput. Vis., July, 2024

MC-PanDA: Mask Confidence for Panoptic Domain Adaptation.
Proceedings of the Computer Vision - ECCV 2024, 2024

2023
Dense Semantic Forecasting in Video by Joint Regression of Features and Feature Motion.
IEEE Trans. Neural Networks Learn. Syst., September, 2023

Panoptic SwiftNet: Pyramidal Fusion for Real-Time Panoptic Segmentation.
Remote. Sens., April, 2023

On advantages of Mask-level Recognition for Open-set Segmentation in the Wild.
CoRR, 2023

On Advantages of Mask-level Recognition for Outlier-aware Segmentation.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Združeno prognoziranje značajki i njihova pomaka za predviđanje semantičke budućnosti u videu ; Joint forecasting of features and feature motion for semantic future prediction in video.
PhD thesis, 2022

Multi-domain semantic segmentation with overlapping labels <sup>*</sup>.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2022

2021
Multi-domain semantic segmentation with overlapping labels.
CoRR, 2021

Joint Forecasting of Features and Feature Motion for Dense Semantic Future Prediction.
CoRR, 2021

2020
Multi-domain semantic segmentation with pyramidal fusion.
CoRR, 2020

Multimodal Semantic Forecasting Based on Conditional Generation of Future Features.
Proceedings of the Pattern Recognition - 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28, 2020

Warp to the Future: Joint Forecasting of Features and Feature Motion.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

2019
Single Level Feature-to-Feature Forecasting with Deformable Convolutions.
Proceedings of the Pattern Recognition, 2019


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