Self-supervised monocular depth estimation aims to infer depth information
without relying on labeled data. However, the lack of labeled information poses
a significant challenge to the model's representation, limiting its ability to
capture the intricate details of the scene accurately. Prior information can
potentially mitigate this issue, enhancing the model's understanding of scene
structure and texture. Nevertheless, solely relying on a single type of prior
information often falls short when dealing with complex scenes, necessitating
improvements in generalization performance. To address these challenges, we
introduce a novel self-supervised monocular depth estimation model that
leverages multiple priors to bolster representation capabilities across
spatial, context, and semantic dimensions. Specifically, we employ a hybrid
transformer and a lightweight pose network to obtain long-range spatial priors
in the spatial dimension. Then, the context prior attention is designed to
improve generalization, particularly in complex structures or untextured areas.
In addition, semantic priors are introduced by leveraging semantic boundary
loss, and semantic prior attention is supplemented, further refining the
semantic features extracted by the decoder. Experiments on three diverse
datasets demonstrate the effectiveness of the proposed model. It integrates
multiple priors to comprehensively enhance the representation ability,
improving the accuracy and reliability of depth estimation. Codes are available
at: https://github.com/MVME-HBUT/MPRLNet

通过利用多个先验知识来增强表示能力，我们提出了一种新颖的自监督单目深度估计模型，以综合提高深度估计的准确性和可靠性。

基于混合 Transformer 的自监督单目深度估计的多先验表示学习

Multiple Prior Representation Learning for Self-Supervised Monocular  Depth Estimation via Hybrid Transformer

Graph neural networks (GNNs) have gained significant interest in recent years
due to their ability to handle arbitrarily structured data represented as
graphs. GNNs generally follow the message-passing scheme to locally update node
feature representations. A graph readout function is then employed to create a
representation for the entire graph. Several studies proposed different GNNs by
modifying the aggregation and combination strategies of the message-passing
framework, often inspired by heuristics. Nevertheless, several studies have
begun exploring GNNs from a theoretical perspective based on the graph
isomorphism problem which inherently assumes countable node feature
representations. Yet, there are only a few theoretical works exploring GNNs
with uncountable node feature representations. This paper presents a new
perspective on the representational capabilities of GNNs across all levels -
node-level, neighborhood-level, and graph-level - when the space of node
feature representation is uncountable. From the results, a novel
soft-isomorphic relational graph convolution network (SIR-GCN) is proposed that
emphasizes non-linear and contextualized transformations of neighborhood
feature representations. The mathematical relationship of SIR-GCN and three
widely used GNNs is explored to highlight the contribution. Validation on
synthetic datasets then demonstrates that SIR-GCN outperforms comparable models
even in simple node and graph property prediction tasks.

本论文从理论角度探索了具有不可数节点特征表示的图神经网络的表征能力，提出了一种新颖的软同构关系图卷积网络（SIR-GCN），并通过与其他常用图神经网络的数学关系进行验证，证明其在简单节点和图属性预测任务中优于其他模型。

情境化信息强化图表示

Contextualized Messages Boost Graph Representations

The training of deep residual neural networks (ResNets) with backpropagation
has a memory cost that increases linearly with respect to the depth of the
network. A way to circumvent this issue is to use reversible architectures. In
this paper, we propose to change the forward rule of a ResNet by adding a
momentum term. The resulting networks, momentum residual neural networks
(Momentum ResNets), are invertible. Unlike previous invertible architectures,
they can be used as a drop-in replacement for any existing ResNet block. We
show that Momentum ResNets can be interpreted in the infinitesimal step size
regime as second-order ordinary differential equations (ODEs) and exactly
characterize how adding momentum progressively increases the representation
capabilities of Momentum ResNets. Our analysis reveals that Momentum ResNets
can learn any linear mapping up to a multiplicative factor, while ResNets
cannot. In a learning to optimize setting, where convergence to a fixed point
is required, we show theoretically and empirically that our method succeeds
while existing invertible architectures fail. We show on CIFAR and ImageNet
that Momentum ResNets have the same accuracy as ResNets, while having a much
smaller memory footprint, and show that pre-trained Momentum ResNets are
promising for fine-tuning models.

本研究提出了一种新的神经网络结构，称为动量残差神经网络 (Momentum ResNets)，其采用可逆性结构和动量机制，优于现有的可逆结构，能够学习任何线性映射并在 CIFAR 和 ImageNet 上具有与 ResNets 相同的准确度，且具有更小的内存占用。