The Copernicus Regional Reanalysis for Europe, CERRA, is a high-resolution
regional reanalysis dataset for the European domain. In recent years it has
shown significant utility across various climate-related tasks, ranging from
forecasting and climate change research to renewable energy prediction,
resource management, air quality risk assessment, and the forecasting of rare
events, among others. Unfortunately, the availability of CERRA is lagging two
years behind the current date, due to constraints in acquiring the requisite
external data and the intensive computational demands inherent in its
generation. As a solution, this paper introduces a novel method using diffusion
models to approximate CERRA downscaling in a data-driven manner, without
additional informations. By leveraging the lower resolution ERA5 dataset, which
provides boundary conditions for CERRA, we approach this as a super-resolution
task. Focusing on wind speed around Italy, our model, trained on existing CERRA
data, shows promising results, closely mirroring original CERRA data.
Validation with in-situ observations further confirms the model's accuracy in
approximating ground measurements.

该论文介绍了一种使用扩散模型来近似欧洲地区高分辨率再分析数据集 CERRA 的下采样方法，借助低分辨率 ERA5 数据集作为边界条件，通过模型训练与现有 CERRA 数据相似，通过与实测数据的验证进一步证实了模型的准确性。

风速超分辨率和验证：从 ERA5 到 CERRA 的扩散模型

Wind speed super-resolution and validation: from ERA5 to CERRA via  diffusion models

Heatmap-based methods have become the mainstream method for pose estimation
due to their superior performance. However, heatmap-based approaches suffer
from significant quantization errors with downscale heatmaps, which result in
limited performance and the detrimental effects of intermediate supervision.
Previous heatmap-based methods relied heavily on additional post-processing to
mitigate quantization errors. Some heatmap-based approaches improve the
resolution of feature maps by using multiple costly upsampling layers to
improve localization precision. To solve the above issues, we creatively view
the backbone network as a degradation process and thus reformulate the heatmap
prediction as a Super-Resolution (SR) task. We first propose the SR head, which
predicts heatmaps with a spatial resolution higher than the input feature maps
(or even consistent with the input image) by super-resolution, to effectively
reduce the quantization error and the dependence on further post-processing.
Besides, we propose SRPose to gradually recover the HR heatmaps from LR
heatmaps and degraded features in a coarse-to-fine manner. To reduce the
training difficulty of HR heatmaps, SRPose applies SR heads to supervise the
intermediate features in each stage. In addition, the SR head is a lightweight
and generic head that applies to top-down and bottom-up methods. Extensive
experiments on the COCO, MPII, and CrowdPose datasets show that SRPose
outperforms the corresponding heatmap-based approaches. The code and models are
available at this https URL

基于热图的方法已经成为姿势估计的主流方法，但由于通过降低重要热图的大小而带来量化误差的原因，这些方法受到了一定的限制，而且中间监督也起到了有害效果。为了解决以上问题，该论文提出了 SRPose 方法，通过将骨干网络视为降级过程并重新构建热图预测作为超分辨率任务，从而有效降低了量化误差和进一步后处理的依赖。实验结果显示 SRPose 在 COCO、MPII 和 CrowdPose 数据集上的性能优于其他基于热图的方法。

人体姿态估计轻量级超分辨率头

Lightweight Super-Resolution Head for Human Pose Estimation

By exploiting large kernel decomposition and attention mechanisms,
convolutional neural networks (CNN) can compete with transformer-based methods
in many high-level computer vision tasks. However, due to the advantage of
long-range modeling, the transformers with self-attention still dominate the
low-level vision, including the super-resolution task. In this paper, we
propose a CNN-based multi-scale attention network (MAN), which consists of
multi-scale large kernel attention (MLKA) and a gated spatial attention unit
(GSAU), to improve the performance of convolutional SR networks. Within our
MLKA, we rectify LKA with multi-scale and gate schemes to obtain the abundant
attention map at various granularity levels, therefore jointly aggregating
global and local information and avoiding the potential blocking artifacts. In
GSAU, we integrate gate mechanism and spatial attention to remove the
unnecessary linear layer and aggregate informative spatial context. To confirm
the effectiveness of our designs, we evaluate MAN with multiple complexities by
simply stacking different numbers of MLKA and GSAU. Experimental results
illustrate that our MAN can achieve varied trade-offs between state-of-the-art
performance and computations. Code is available at
this https URL

本文提出了一个卷积神经网络（CNN）的多尺度关注网络（MAN），其中包括多尺度大型核关注（MLKA）和门控空间关注单元（GSAU），以提高卷积 SR 网络的性能。实验结果表明，我们的 MAN 可以在不同的状态下实现各种平衡。