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.