In this paper, we address the problem of global-scale image geolocation,
proposing a mixed classification-retrieval scheme. Unlike other methods that
strictly tackle the problem as a classification or retrieval task, we combine
the two practices in a unified solution leveraging the advantages of each
approach with two different modules. The first leverages the EfficientNet
architecture to assign images to a specific geographic cell in a robust way.
The second introduces a new residual architecture that is trained with
contrastive learning to map input images to an embedding space that minimizes
the pairwise geodesic distance of same-location images. For the final location
estimation, the two modules are combined with a search-within-cell scheme,
where the locations of most similar images from the predicted geographic cell
are aggregated based on a spatial clustering scheme. Our approach demonstrates
very competitive performance on four public datasets, achieving new
state-of-the-art performance in fine granularity scales, i.e., 15.0% at 1km
range on Im2GPS3k.

本文提出了一种混合分类 - 检索方案，其中利用 EfficientNet 架构将图像分配到特定的地理单元，并使用训练有素的残差架构将输入图像映射到一个嵌入空间，以最小化相同位置图像的配对测地线距离，然后将两个模块与空间聚类方案相结合，通过搜索 - 单元内的聚合确定最终位置估计。结果表明，该方法的性能非常竞争，在 4 个公共数据集上均能取得新的最优表现。

利用 EfficientNet 和对比学习进行精准的全球位置估计

Leveraging EfficientNet and Contrastive Learning for Accurate  Global-scale Location Estimation

In recent years, waveform-mapping-based speech enhancement (SE) methods have
garnered significant attention. These methods generally use a deep learning
model to directly process and reconstruct speech waveforms. Because both the
input and output are in waveform format, the waveform-mapping-based SE methods
can overcome the distortion caused by imperfect phase estimation, which may be
encountered in spectral-mapping-based SE systems. So far, most
waveform-mapping-based SE methods have focused on single-channel tasks. In this
paper, we propose a novel fully convolutional network (FCN) with Sinc and
dilated convolutional layers (termed SDFCN) for multichannel SE that operates
in the time domain. We also propose an extended version of SDFCN, called the
residual SDFCN (termed rSDFCN). The proposed methods are evaluated on two
multichannel SE tasks, namely the dual-channel inner-ear microphones SE task
and the distributed microphones SE task. The experimental results confirm the
outstanding denoising capability of the proposed SE systems on both tasks and
the benefits of using the residual architecture on the overall SE performance.

本文提出了一种基于 Sinc 和 dilated 卷积层的全卷积网络（称为 SDFCN）及其扩展版本的残差 SDFCN（称为 rSDFCN），用于时间域下的多通道语音增强（multichannel SE）任务，实验结果表明所提出的语音增强系统在双通道内耳麦和分布式麦克风静音任务中具有优异的性能。