As an important task in remote sensing image analysis, remote sensing change
detection (RSCD) aims to identify changes of interest in a region from
spatially co-registered multi-temporal remote sensing images, so as to monitor
the local development. Existing RSCD methods usually formulate RSCD as a binary
classification task, representing changes of interest by merely feature
concatenation or feature subtraction and recovering the spatial details via
densely connected change representations, whose performances need further
improvement. In this paper, we propose STNet, a RSCD network based on spatial
and temporal feature fusions. Specifically, we design a temporal feature fusion
(TFF) module to combine bi-temporal features using a cross-temporal gating
mechanism for emphasizing changes of interest; a spatial feature fusion module
is deployed to capture fine-grained information using a cross-scale attention
mechanism for recovering the spatial details of change representations.
Experimental results on three benchmark datasets for RSCD demonstrate that the
proposed method achieves the state-of-the-art performance. Code is available at
this https URL

本篇论文提出了一种基于时空特征融合的 RSCD 网络 STNet，通过时间特征融合模块和空间特征融合模块强化感兴趣的变化并恢复变化表征的空间细节，实验表明该方法在三个基准数据集上取得了最先进的性能。

STNet：用于遥感图像变化检测的时空特征融合网络

STNet: Spatial and Temporal feature fusion network for change detection  in remote sensing images

Human pose estimation has achieved significant progress in recent years.
However, most of the recent methods focus on improving accuracy using
complicated models and ignoring real-time efficiency. To achieve a better
trade-off between accuracy and efficiency, we propose a novel neural
architecture search (NAS) method, termed ViPNAS, to search networks in both
spatial and temporal levels for fast online video pose estimation. In the
spatial level, we carefully design the search space with five different
dimensions including network depth, width, kernel size, group number, and
attentions. In the temporal level, we search from a series of temporal feature
fusions to optimize the total accuracy and speed across multiple video frames.
To the best of our knowledge, we are the first to search for the temporal
feature fusion and automatic computation allocation in videos. Extensive
experiments demonstrate the effectiveness of our approach on the challenging
COCO2017 and PoseTrack2018 datasets. Our discovered model family, S-ViPNAS and
T-ViPNAS, achieve significantly higher inference speed (CPU real-time) without
sacrificing the accuracy compared to the previous state-of-the-art methods.

我们提出了一种新颖的神经架构搜索方法 ViPNAS，用于快速在线视频姿势估计，通过在空间和时间级别上搜索网络来实现更好的准确度和效率之间的权衡。在时间级别上，我们首次搜索了视频中的时间特征融合和自动计算分配。我们的实验结果表明，我们的方法在 CPU 实时推导速度方面比先前的最先进方法快而不牺牲准确性。