Geo-localization Framework for Real-world Scenarios - Defense Presentation

Watch a 39-minute defense presentation by Sijie Zhu from the University of Central Florida on geo-localization frameworks. Explore the challenges of real-world scenarios, compare datasets, and learn about a novel geo-localization framework. Discover a new loss function that leverages multiple references and delve into orientation definition and estimation. Examine the predominant triplet-based loss and its adjustments for similarity distribution. Investigate methods to bridge the domain gap, including Vision Transformers and non-uniform cropping. Analyze retrieval performance on the VIGOR dataset, including meter-level evaluation and unknown orientation scenarios. Gain insights through visualizations and qualitative results presented in this comprehensive academic presentation.

Intro
Education Background
Overview
Toward Real-world Scenarios
Datasets Comparison
A Novel Geo-localization Framework
Novel Loss to Leverage Multiple Reference
Orientation Definition
Revisiting the Orientation Issue
The Predominant Triplet-based Loss
Better Adjustment on Similarity Distribution
Estimate the Orientation
Better Visual Explanation and Orientation Estimatio
How to Bridge the Domain Gap?
Vision Transformer (VIT)
Non-uniform Cropping
Retrieval Performance on VIGOR
Meter-level Evaluation
Unknown Orientation
Visualization
Qualitative Results-VIGOR