The dominant CNN-based methods for cross-view image geo-localization rely on polar
transform and fail to model global correlation. We propose a pure transformer-based …

深度强化学习主要被用来处理感知− 决策问题, 已经成为人工智能领域重要的研究分支.
概述了基于值函数和策略梯度的两类深度强化学习算法, 详细阐述了深度Q 网络 …

Neural networks are achieving state of the art and sometimes super-human performance on
learning tasks across a variety of domains. Whenever these problems require learning in a …

Although multi-view learning has made significant progress over the past few decades, it is
still challenging due to the difficulty in modeling complex correlations among different views …

Cross-view image geo-localization aims to determine the locations of street-view query
images by matching with GPS-tagged reference images from aerial view. Recent works …

In this paper, we introduce a novel approach to fine-grained cross-view geo-localization. Our
method aligns a warped ground image with a corresponding GPS-tagged satellite image …

Street-to-aerial image geo-localization, which matches a query street-view image to the GPS-
tagged aerial images in a reference set, has attracted increasing attention recently. In this …

This paper investigates the effective utilization of unlabeled data for large-area cross-view
geo-localization (CVGL) encompassing both unsupervised and semi-supervised settings …

Navigating and understanding the real world remains a key challenge in machine learning
and inspires a great variety of research in areas such as language grounding, planning …

With the recent development of deep learning, AI models are widely used in various
domains. AI models show good performance for definite tasks such as image classification …