原文地址：https://pub.towardsai.net/understanding-mamba-and-selective-state-space-models-ssms-1519c...
Mamba模型中，"A"、"B"、"C"和"D"分别代表状态空间模型（State Space Models，简称SSMs）的参数。这...
We motivate our selection mechanism using intuition from synthetic tasks (Section 3.1), then explain how to incorporate this mechanism into state space models (Section 3.2). The resulting time-varying SSMs cannot use convolutions, presenting a technical challenge of how to compute them efficiently. ...
2 State Space Models 3 Selective State Space Models and 3.1 Motivation: Selection as a Means of Compression 3.2 Improving SSMs with Selection 3.3 Efficient Implementation of Selective SSMs 3.4 A Simplified SSM Architecture 3.5 Properties of Selection Mechanisms ...
Section 3 Selective State Space Models 选择性状态空间模型 第3.1 节:利用合成任务的直觉来激发我们的选择机制, 第3.2 节:解释如何将这一机制纳入状态空间模型。 第3.3 节:由此产生的时变 SSM 不能使用卷积,这就提出了如何高效计算的技术难题。本文采用一种硬件感知算法,利用现代硬件的内存层次结构来克服这一难题...
Inspired by the fact that humans count objects in high-resolution images by sequential scanning, we explore the potential of handling plant counting tasks via state space models (SSMs) for generating counting results. In this paper, we propose a new counting approach named CountMamba that ...
Structured state-space models (SSMs) such as S4, stemming from the seminal work of Gu et al., are gaining popularity as effective approaches for modeling sequential data. Deep SSMs demonstrate outstanding performance across a diverse set of domains, at a reduced training and inference cost compare...
In this report, we identify the inability of these models to perform content-based reasoning as a key weakness and focus on Mamba, a novel neural network architecture that integrates selective structured state space models (SSMs) to address this limitation....
(i.e., rate-distortion trade-off) and efficiency remains a challenge. Recently, state-space models (SSMs) have shown promise due to their long-range modeling capacity and efficiency. Inspired by this, we take the first step to explore SSMs for visual compression. We introduce MambaVC, a ...
10. The results reveal that our LSKNet-T and LSKNet-S perform exceptionally well, achieving state- of-the-art mAP scores of 46.93% and 47.87% respectively, surpassing all other models by a significant margin. 4.5. Analysis Detection Results Visualization. Visualization ...