266 citations · 888 across the 101 of their papers we have counts for
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Using Auxiliary Information for Person Re-Identification -- A Tutorial Overview
Tharindu Fernando, Clinton Fookes, Sridha Sridharan +1
Person re-identification (re-id) is a pivotal task within an intelligent surveillance pipeline and there exist numerous re-id frameworks that achieve satisfactory performance in ch…
Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments
Joshua Knights, Kavisha Vidanapathirana, Milad Ramezani +3
Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning base…
Spectral Geometric Verification: Re-Ranking Point Cloud Retrieval for Metric Localization
Kavisha Vidanapathirana, Peyman Moghadam, Sridha Sridharan +1
In large-scale metric localization, an incorrect result during retrieval will lead to an incorrect pose estimate or loop closure. Re-ranking methods propose to take into account al…
SESS: Saliency Enhancing with Scaling and Sliding
Osman Tursun, Simon Denman, Sridha Sridharan +1
High-quality saliency maps are essential in several machine learning application areas including explainable AI and weakly supervised object detection and segmentation. Many techni…
CorticalFlow: Boosting Cortical Surface Reconstruction Accuracy, Regularity, and Interoperability
Rodrigo Santa Cruz, Léo Lebrat, Darren Fu +4
The problem of Cortical Surface Reconstruction from magnetic resonance imaging has been traditionally addressed using lengthy pipelines of image processing techniques like FreeSurf…
Does Interference Exist When Training a Once-For-All Network?
Jordan Shipard, Arnold Wiliem, Clinton Fookes
The Once-For-All (OFA) method offers an excellent pathway to deploy a trained neural network model into multiple target platforms by utilising the supernet-subnet architecture. Onc…