Relational Representation Learning Network for Cross-Spectral Image
  Patch Matching
Chuang Yu Yunpeng Liu Jinmiao Zhao Dou Quan Zelin Shi
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.