Remote sensing image change detection aims to identify the differences
between images acquired at different times in the same area. It is widely used
in land management, environmental monitoring, disaster assessment and other
fields. Currently, most change detection methods are based on Siamese network
structure or early fusion structure. Siamese structure focuses on extracting
object features at different times but lacks attention to change information,
which leads to false alarms and missed detections. Early fusion (EF) structure
focuses on extracting features after the fusion of images of different phases
but ignores the significance of object features at different times for
detecting change details, making it difficult to accurately discern the edges
of changed objects. To address these issues and obtain more accurate results,
we propose a novel network, Triplet UNet(T-UNet), based on a three-branch
encoder, which is capable to simultaneously extract the object features and the
change features between the pre- and post-time-phase images through triplet
encoder. To effectively interact and fuse the features extracted from the three
branches of triplet encoder, we propose a multi-branch spatial-spectral
cross-attention module (MBSSCA). In the decoder stage, we introduce the channel
attention mechanism (CAM) and spatial attention mechanism (SAM) to fully mine
and integrate detailed textures information at the shallow layer and semantic
localization information at the deep layer.

远程感知图像变化检测中存在虚警和缺失检测的问题。本论文提出了一种新型网络 Triplet UNet (T-UNet)，通过三支编码器同时提取目标特征和前后时间相位图像之间的变化特征。为了有效地交互和融合三支编码器提取的特征，我们提出了一个多支空间 - 光谱交叉注意力模块 (MBSSCA)。在解码器阶段，引入了通道注意力机制 (CAM) 和空间注意力机制 (SAM)，以充分挖掘浅层的细节纹理信息和深层的语义定位信息。

T-UNet：用于高分辨率遥感影像变化检测的三元 UNet

T-UNet: Triplet UNet for Change Detection in High-Resolution Remote  Sensing Images

Nonparallel multi-domain voice conversion methods such as the StarGAN-VCs
have been widely applied in many scenarios. However, the training of these
models usually poses a challenge due to their complicated adversarial network
architectures. To address this, in this work we leverage the state-of-the-art
contrastive learning techniques and incorporate an efficient Siamese network
structure into the StarGAN discriminator. Our method is called
SimSiam-StarGAN-VC and it boosts the training stability and effectively
prevents the discriminator overfitting issue in the training process. We
conduct experiments on the Voice Conversion Challenge (VCC 2018) dataset, plus
a user study to validate the performance of our framework. Our experimental
results show that SimSiam-StarGAN-VC significantly outperforms existing
StarGAN-VC methods in terms of both the objective and subjective metrics.

本文提出了一种名为 SimSiam-StarGAN-VC 的方法，利用对比学习和高效的同构网络结构解决了 StarGAN-VCs 模型训练过程中存在的挑战，实验结果表明该模型在客观和主观评价指标上都显著优于现有的 StarGAN-VC 方法。