The safe and stable operation of power systems is greatly challenged by the
high variability and randomness of wind power in large-scale
wind-power-integrated grids. Wind power forecasting is an effective solution to
tackle this issue, with wind speed forecasting being an essential aspect. In
this paper, a Graph-attentive Frequency-enhanced Spatial-Temporal Wind Speed
Forecasting model based on graph attention and frequency-enhanced mechanisms,
i.e., GFST-WSF, is proposed to improve the accuracy of short-term wind speed
forecasting. The GFST-WSF comprises a Transformer architecture for temporal
feature extraction and a Graph Attention Network (GAT) for spatial feature
extraction. The GAT is specifically designed to capture the complex spatial
dependencies among wind speed stations to effectively aggregate information
from neighboring nodes in the graph, thus enhancing the spatial representation
of the data. To model the time lag in wind speed correlation between adjacent
wind farms caused by geographical factors, a dynamic complex adjacency matrix
is formulated and utilized by the GAT. Benefiting from the effective
spatio-temporal feature extraction and the deep architecture of the
Transformer, the GFST-WSF outperforms other baselines in wind speed forecasting
for the 6-24 hours ahead forecast horizon in case studies.

本文提出了一种基于图注意力和频率增强机制的图注意力频率增强时空风速预测模型 (GFST-WSF)，该模型采用变压器架构进行时间特征提取，并采用图注意力网络 (GAT) 进行空间特征提取，从而提高短期风速预测的准确性。在案例研究中，GFST-WSF 在 6-24 小时预测范围内的风速预测中表现出比其他基线更好的性能。

利用图注意力和频率增强机制提高短期风速预测的准确性

Enhancing Short-Term Wind Speed Forecasting using Graph Attention and  Frequency-Enhanced Mechanisms

This report presents the technical details of our submission to the
EPIC-Kitchens-100 Action Recognition Challenge 2021. To participate in the
challenge we deployed spatio-temporal feature extraction and aggregation models
we have developed recently: GSF and XViT. GSF is an efficient spatio-temporal
feature extracting module that can be plugged into 2D CNNs for video action
recognition. XViT is a convolution free video feature extractor based on
transformer architecture. We design an ensemble of GSF and XViT model families
with different backbones and pretraining to generate the prediction scores. Our
submission, visible on the public leaderboard, achieved a top-1 action
recognition accuracy of 44.82%, using only RGB.

本文讨论了我们参加 EPIC-Kitchens-100 行为识别挑战的技术细节，其中我们使用 GSF 和 XViT 等模型进行空间 - 时间特征提取和聚合，设计了不同 Backbones 和 Pretraining 的混合模型，并使用 RGB 完成了具有 44.82% 准确率的 top-1 行为识别。

SAIC_Cambridge-HuPBA-FBK 参与 EPIC-Kitchens-100 动作识别挑战赛 2021

SAIC_Cambridge-HuPBA-FBK Submission to the EPIC-Kitchens-100 Action  Recognition Challenge 2021

In this report we describe the technical details of our submission to the
EPIC-Kitchens Action Recognition 2020 Challenge. To participate in the
challenge we deployed spatio-temporal feature extraction and aggregation models
we have developed recently: Gate-Shift Module (GSM) [1] and EgoACO, an
extension of Long Short-Term Attention (LSTA) [2]. We design an ensemble of GSM
and EgoACO model families with different backbones and pre-training to generate
the prediction scores. Our submission, visible on the public leaderboard with
team name FBK-HUPBA, achieved a top-1 action recognition accuracy of 40.0% on
S1 setting, and 25.71% on S2 setting, using only RGB.

本文介绍了我们参加 EPIC-Kitchens 2020 挑战赛所使用的技术细节，我们使用 Gate-Shift 模块和 EgoACO 来产生预测分数，最终我们的提交记录在公共排行榜上取得了 40.0％的 top-1 动作识别精度，使用了仅 RGB 的 S1 设置和 25.71％的 S2 设置。