Recently, feature relation learning has drawn widespread attention in
cross-spectral image patch matching. However, existing related research focuses
on extracting diverse relations between image patch features and ignores
sufficient intrinsic feature representations of individual image patches.
Therefore, an innovative relational representation learning idea is proposed
for the first time, which simultaneously focuses on sufficiently mining the
intrinsic features of individual image patches and the relations between image
patch features. Based on this, we construct a lightweight Relational
Representation Learning Network (RRL-Net). Specifically, we innovatively
construct an autoencoder to fully characterize the individual intrinsic
features, and introduce a Feature Interaction Learning (FIL) module to extract
deep-level feature relations. To further fully mine individual intrinsic
features, a lightweight Multi-dimensional Global-to-Local Attention (MGLA)
module is constructed to enhance the global feature extraction of individual
image patches and capture local dependencies within global features. By
combining the MGLA module, we further explore the feature extraction network
and construct an Attention-based Lightweight Feature Extraction (ALFE) network.
In addition, we propose a Multi-Loss Post-Pruning (MLPP) optimization strategy,
which greatly promotes network optimization while avoiding increases in
parameters and inference time. Extensive experiments demonstrate that our
RRL-Net achieves state-of-the-art (SOTA) performance on multiple public
datasets. Our code will be made public later.

通过创新的关系表示学习方法，同时关注个体内在特征和图像补丁特征之间的关系，构建了一种轻量级的关系表示学习网络（RRL-Net），包括自动编码器、特征交互学习模块、多维全局到局部注意力机制以及基于注意力的轻量级特征提取网络。经过大量实验验证，该网络在多个公共数据集上取得了最先进的性能。