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。

跨光谱图像块匹配的关系表示学习网络

Relational Representation Learning Network for Cross-Spectral Image  Patch Matching

In this work, we move beyond the traditional complex-valued representations,
introducing more expressive hypercomplex representations to model entities and
relations for knowledge graph embeddings. More specifically, quaternion
embeddings, hypercomplex-valued embeddings with three imaginary components, are
utilized to represent entities. Relations are modelled as rotations in the
quaternion space. The advantages of the proposed approach are: (1) Latent
inter-dependencies (between all components) are aptly captured with Hamilton
product, encouraging a more compact interaction between entities and relations;
(2) Quaternions enable expressive rotation in four-dimensional space and have
more degree of freedom than rotation in complex plane; (3) The proposed
framework is a generalization of ComplEx on hypercomplex space while offering
better geometrical interpretations, concurrently satisfying the key desiderata
of relational representation learning (i.e., modeling symmetry, anti-symmetry
and inversion). Experimental results demonstrate that our method achieves
state-of-the-art performance on four well-established knowledge graph
completion benchmarks.

本研究提出了一种基于超复数表示的知识图谱嵌入方法，利用四元数来表示实体并将关系建模为四维空间中的旋转方式，实验证明此方法在关系表示学习方面表现出色并达到了当前领域最好的水平。

四元数知识图谱嵌入

Quaternion Knowledge Graph Embeddings

Deep learning methods capable of handling relational data have proliferated
over the last years. In contrast to traditional relational learning methods
that leverage first-order logic for representing such data, these deep learning
methods aim at re-representing symbolic relational data in Euclidean spaces.
They offer better scalability, but can only numerically approximate relational
structures and are less flexible in terms of reasoning tasks supported. This
paper introduces a novel framework for relational representation learning that
combines the best of both worlds. This framework, inspired by the auto-encoding
principle, uses first-order logic as a data representation language, and the
mapping between the original and latent representation is done by means of
logic programs instead of neural networks. We show how learning can be cast as
a constraint optimisation problem for which existing solvers can be used. The
use of logic as a representation language makes the proposed framework more
accurate (as the representation is exact, rather than approximate), more
flexible, and more interpretable than deep learning methods. We experimentally
show that these latent representations are indeed beneficial in relational
learning tasks.

本研究提出了一种新颖的用于关系表示学习的框架，该框架结合了传统的一阶逻辑和自动编码原理，使用逻辑程序而非神经网络进行原始表示和潜在表示之间的映射，将学习视为约束优化问题，得到的潜在表示在关系学习任务中表现优异，相较于深度学习方法，该框架更为准确、可解释性更强、更具灵活性。