Cross-view geo-localization (CVGL) task aims to utilize geographic data, such as maps or high-resolution satellite images, as reference to estimate the positions of a ground- or near-ground- captured query image. This task is particularly challenging due to the significant changes in visual appearance resulting from the extreme viewpoint variations. To address this challenge, a range of innovative methods have been proposed. However, intra-scene geometric information and inter-scene discriminative representation are not fully explored. In this article, we propose a novel CVGL method using contrastive attributes mining and position-aware partitioning (CAMP), which incorporates a position-aware partition branch (PPB) and a contrastive attributes mining (CAM) strategy. PPB learns fine-grained local features of different parts and captures their spatial information, providing a comprehensive understanding of scenes from both textual and spatial perspectives. CAM establishes supervision of the negative samples based on the images from the same platform, empowering the model to better discern differences between distinct scenes without extra memory cost. The proposed CAMP surpasses existing methods, achieving state-of-the-art results on the satellite-drone CVGL datasets University-1652 and SUES-200. Additionally, our method also outperforms existing methods in cross-dataset generalization, achieving an 8.85% increase in R@1 when trained on the University-1652 dataset and tested on the SUES-200 dataset at a height of 150 m. Our code and model are available athttps://github.com/Mabel0403/CAMP.

Convolutional Neural Networks (CNNs) are widely deployed on the Graphics Processing Unit (GPU) to support Deep Learning (DL) based services. Popular DL frameworks usually ignore the inter-operator parallelism when executing the inference of CNNs, which results in high inference latency. Although some inter-operator scheduling methods have been proposed, there remains a critical trade-off issue between inference latency (effectiveness) and scheduling time (efficiency). In this article, we propose LIOS, a novel latency-based heuristic inter-operator scheduling method to balance inference latency and scheduling time. In LIOS, a CNN latency model is built based on the given CNN and GPU. Then every operator is assigned a priority value to represent its importance. During each iteration of the scheduling process, LIOS identifies the current data-independent operators, selects the operator with the highest priority value, and assigns it to the GPU stream with the smallest finish time. Extensive experimental results have demonstrated the effectiveness and efficiency of LIOS. For the effectiveness, LIOS can speed up the inference of normal-size and large-size CNNs by 1.13$\sim 1.59 \times$compared to sequential scheduling. This result is comparable to IOS, the latest state-of-the-art scheduling method. For the efficiency, LIOS can speed up the scheduling process by 7$\sim 9210\times$compared to IOS.

Because of optical distortion induced by atmospheric turbulence and the limitations of optical devices, acquired images of space objects are blurred and degraded. This effect results in anamorphosis in the output of two-dimensional imaging systems. In this work, we present a novel Enhanced Alignment Fusion-Wasserstein Generative Adversarial Network, called EAF-WGAN, for turbulent image restoration. This model is characterized by the innovative use of two modules, including the Align Module (AM) and the Feature Fusion Module (FFM) in the generator, especially in the process of feature fusion, in which 3D convolution is used. Through 3D convolution, the temporal and spatial information of the input image is obtained. Therefore, the formation mode and intensity of turbulence are not considered and the image can be reconstructed. The ability of EAF-WGAN is proved by algorithmically simulated data, physically simulated data, and real-world data.