Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) stand as
the two most popular foundation models for visual representation learning.
While CNNs exhibit remarkable scalability with linear complexity w.r.t. image
resolution, ViTs surpass them in fitting capabilities despite contending with
quadratic complexity. A closer inspection reveals that ViTs achieve superior
visual modeling performance through the incorporation of global receptive
fields and dynamic weights. This observation motivates us to propose a novel
architecture that inherits these components while enhancing computational
efficiency. To this end, we draw inspiration from the recently introduced state
space model and propose the Visual State Space Model (VMamba), which achieves
linear complexity without sacrificing global receptive fields. To address the
encountered direction-sensitive issue, we introduce the Cross-Scan Module (CSM)
to traverse the spatial domain and convert any non-causal visual image into
order patch sequences. Extensive experimental results substantiate that VMamba
not only demonstrates promising capabilities across various visual perception
tasks, but also exhibits more pronounced advantages over established benchmarks
as the image resolution increases. Source code has been available at
this https URL

基于卷积神经网络和视觉变换器，提出了一种名为 Visual State Space Model (VMamba) 的新型体系结构，具有线性复杂度和全局感受野，并在图像分辨率增加时展现出更明显的优势。

VMamba：视觉状态空间模型

VMamba: Visual State Space Model

Belief Propagation (BP) is an important message-passing algorithm for various
reasoning tasks over graphical models, including solving the Constraint
Optimization Problems (COPs). It has been shown that BP can achieve
state-of-the-art performance on various benchmarks by mixing old and new
messages before sending the new one, i.e., damping. However, existing methods
of tuning a static damping factor for BP not only are laborious but also harm
their performance. Moreover, existing BP algorithms treat each variable node's
neighbors equally when composing a new message, which also limits their
exploration ability. To address these issues, we seamlessly integrate BP, Gated
Recurrent Units (GRUs), and Graph Attention Networks (GATs) within the
message-passing framework to reason about dynamic weights and damping factors
for composing new BP messages. Our model, Deep Attentive Belief Propagation
(DABP), takes the factor graph and the BP messages in each iteration as the
input and infers the optimal weights and damping factors through GRUs and GATs,
followed by a multi-head attention layer. Furthermore, unlike existing
neural-based BP variants, we propose a novel self-supervised learning algorithm
for DABP with a smoothed solution cost, which does not require expensive
training labels and also avoids the common out-of-distribution issue through
efficient online learning. Extensive experiments show that our model
significantly outperforms state-of-the-art baselines.

通过将 Belief Propagation、门控循环单元和图注意力网络无缝地整合进消息传递框架中，我们提出了一种名为 DABP 的深度注意力置信传播模型，它可以推断得到优化后的权重和阻尼因子，通过无需昂贵的训练标签，可有效避免常见的分布问题进行自我监督学习，并通过实验表明它显著优于现有的基线模型。