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20162026
most citedLoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition

108 citations · 346 across the 72 of their papers we have counts for

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Showing 2021 · cs.CVShow all

10 papers · 2 filters

cs.CV2021

Point Cloud Segmentation Using Sparse Temporal Local Attention

Joshua Knights, Peyman Moghadam, Clinton Fookes +1

Point clouds are a key modality used for perception in autonomous vehicles, providing the means for a robust geometric understanding of the surrounding environment. However despite…

cs.CV2021★ 108 cited

LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition

Kavisha Vidanapathirana, Milad Ramezani, Peyman Moghadam +2

Retrieval-based place recognition is an efficient and effective solution for re-localization within a pre-built map, or global data association for Simultaneous Localization and Ma…

cs.CV2021

Discriminative Domain-Invariant Adversarial Network for Deep Domain Generalization

Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan

Domain generalization approaches aim to learn a domain invariant prediction model for unknown target domains from multiple training source domains with different distributions. Sig…

cs.CV2021

Semantic Consistency and Identity Mapping Multi-Component Generative Adversarial Network for Person Re-Identification

Amena Khatun, Simon Denman, Sridha Sridharan +1

In a real world environment, person re-identification (Re-ID) is a challenging task due to variations in lighting conditions, viewing angles, pose and occlusions. Despite recent pe…

cs.CV2021

Pose-driven Attention-guided Image Generation for Person Re-Identification

Amena Khatun, Simon Denman, Sridha Sridharan +1

Person re-identification (re-ID) concerns the matching of subject images across different camera views in a multi camera surveillance system. One of the major challenges in person…

cs.CV2021

Preserving Semantic Consistency in Unsupervised Domain Adaptation Using Generative Adversarial Networks

Mohammad Mahfujur Rahman, Clinton Fookes, Sridha Sridharan

Unsupervised domain adaptation seeks to mitigate the distribution discrepancy between source and target domains, given labeled samples of the source domain and unlabeled samples of…