Learning Quintuplet Loss for Large-scale Visual Geo-Localization
This work addresses the problem of accurate geo-localization in urban computing, but it appears incremental as it builds upon existing metric learning losses.
The paper tackles the challenge of perspective deviation in large-scale visual geo-localization by proposing a new quintuplet loss that embeds potential positive samples into the triplet loss, enhancing various methods with demonstrated effectiveness in experiments.
With the maturity of Artificial Intelligence (AI) technology, Large Scale Visual Geo-Localization (LSVGL) is increasingly important in urban computing, where the task is to accurately and efficiently recognize the geo-location of a given query image. The main challenge of LSVGL faced by many experiments due to the appearance of real-word places may differ in various ways. While perspective deviation almost inevitably exists between training images and query images because of the arbitrary perspective. To cope with this situation, in this paper, we in-depth analyze the limitation of triplet loss which is the most commonly used metric learning loss in state-of-the-art LSVGL framework, and propose a new QUInTuplet Loss (QUITLoss) by embedding all the potential positive samples to the primitive triplet loss. Extensive experiments have been conducted to verify the effectiveness of the proposed approach and the results demonstrate that our new loss can enhance various LSVGL methods.