Instance Segmentation For Autonomous Driving
Instance Segmentation for Autonomous Driving is a specialized computer vision framework that extends generic instance segmentation by incorporating driving-specific contextual constraints, dynamic object handling, and real-time processing requirements to enable precise pixel-level identification and localization of individual objects in complex urban traffic environments.
135
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10.4K
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524
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177
Institutions
Publications per year
2017–2026
125
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Di Feng, Christian Schütz, Lars Rosenbaum et al. · IEEE Transactions on Intelligent Transportation Systems · 2020 · 1.3K citations · Full text
BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling.
Fisher Yu, Wenqi Xian, Yingying Chen et al. · arXiv (Cornell University) · 2018 · 754 citations · Full text
MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
Marvin Teichmann, Michael Weber, Marius Zöllner et al. · 2018 · 693 citations
End-to-End Instance Segmentation with Recurrent Attention
Mengye Ren, Richard S. Zemel · 2017 · 322 citations
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University of Toronto
Toronto, Canada
6 h-index
12 publications
1.6K citations
Valeo (Ireland)
Tuam, Ireland
5 h-index
9 publications
169 citations

Beijing Institute of Technology
Beijing, China
4 h-index
5 publications
335 citations
Tsinghua University
Beijing, China
4 h-index
8 publications
1.1K citations
University of Bonn
Bonn, Germany
4 h-index
17 publications
792 citations
KU Leuven
Leuven, Belgium
3 h-index
11 publications
1.1K citations
University of Oxford
Oxford, United Kingdom
3 h-index
6 publications
483 citations

University of Alberta
Edmonton, Canada
3 h-index
5 publications
864 citations
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Repository · 24 publications · 1.6K citations
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3 publications · 115 citations
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