FusionMamba: Efficient Image Fusion with State Space Model

Image fusion aims to generate a high-resolution multi/hyper-spectral image by combining a high-resolution image with limited spectral information and a low-resolution image with abundant spectral data. Current deep learning (DL)-based methods for image fusion primarily rely on CNNs or Transformers to extract features and merge different types of data. While CNNs are efficient, their receptive fields are limited, restricting their capacity to capture global context. Conversely, Transformers excel at learning global information but are hindered by their quadratic complexity. Fortunately, recent advancements in the State Space Model (SSM), particularly Mamba, offer a promising solution to this issue by enabling global awareness with linear complexity. However, there have been few attempts to explore the potential of SSM in information fusion, which is a crucial ability in domains like image fusion. Therefore, we propose FusionMamba, an innovative method for efficient image fusion. Our contributions mainly focus on two aspects. Firstly, recognizing that images from different sources possess distinct properties, we incorporate Mamba blocks into two U-shaped networks, presenting a novel architecture that extracts spatial and spectral features in an efficient, independent, and hierarchical manner. Secondly, to effectively combine spatial and spectral information, we extend the Mamba block to accommodate dual inputs. This expansion leads to the creation of a new module called the FusionMamba block, which outperforms existing fusion techniques such as concatenation and cross-attention. To validate FusionMamba's effectiveness, we conduct a series of experiments on five datasets related to three image fusion tasks. The quantitative and qualitative evaluation results demonstrate that our method achieves state-of-the-art (SOTA) performance, underscoring the superiority of FusionMamba.

Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening

Currently, machine learning-based methods for remote sensing pansharpening have progressed rapidly. However, existing pansharpening methods often do not fully exploit differentiating regional information in non-local spaces, thereby limiting the effectiveness of the methods and resulting in redundant learning parameters. In this paper, we introduce a so-called content-adaptive non-local convolution (CANConv), a novel method tailored for remote sensing image pansharpening. Specifically, CANConv employs adaptive convolution, ensuring spatial adaptability, and incorporates non-local self-similarity through the similarity relationship partition (SRP) and the partition-wise adaptive convolution (PWAC) sub-modules. Furthermore, we also propose a corresponding network architecture, called CANNet, which mainly utilizes the multi-scale self-similarity. Extensive experiments demonstrate the superior performance of CANConv, compared with recent promising fusion methods. Besides, we substantiate the method's effectiveness through visualization, ablation experiments, and comparison with existing methods on multiple test sets. The source code is publicly available at https://github.com/duanyll/CANConv.

Tensor Decomposition Based Attention Module for Spiking Neural Networks

The attention mechanism has been proven to be an effective way to improve spiking neural network (SNN). However, based on the fact that the current SNN input data flow is split into tensors to process on GPUs, none of the previous works consider the properties of tensors to implement an attention module. This inspires us to rethink current SNN from the perspective of tensor-relevant theories. Using tensor decomposition, we design the \textit{projected full attention} (PFA) module, which demonstrates excellent results with linearly growing parameters. Specifically, PFA is composed by the \textit{linear projection of spike tensor} (LPST) module and \textit{attention map composing} (AMC) module. In LPST, we start by compressing the original spike tensor into three projected tensors using a single property-preserving strategy with learnable parameters for each dimension. Then, in AMC, we exploit the inverse procedure of the tensor decomposition process to combine the three tensors into the attention map using a so-called connecting factor. To validate the effectiveness of the proposed PFA module, we integrate it into the widely used VGG and ResNet architectures for classification tasks. Our method achieves state-of-the-art performance on both static and dynamic benchmark datasets, surpassing the existing SNN models with Transformer-based and CNN-based backbones.

TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks

Spiking Neural Networks (SNNs) is a practical approach toward more data-efficient deep learning by simulating neurons leverage on temporal information. In this paper, we propose the Temporal-Channel Joint Attention (TCJA) architectural unit, an efficient SNN technique that depends on attention mechanisms, by effectively enforcing the relevance of spike sequence along both spatial and temporal dimensions. Our essential technical contribution lies on: 1) compressing the spike stream into an average matrix by employing the squeeze operation, then using two local attention mechanisms with an efficient 1-D convolution to establish temporal-wise and channel-wise relations for feature extraction in a flexible fashion. 2) utilizing the Cross Convolutional Fusion (CCF) layer for modeling inter-dependencies between temporal and channel scope, which breaks the independence of the two dimensions and realizes the interaction between features. By virtue of jointly exploring and recalibrating data stream, our method outperforms the state-of-the-art (SOTA) by up to 15.7% in terms of top-1 classification accuracy on all tested mainstream static and neuromorphic datasets, including Fashion-MNIST, CIFAR10-DVS, N-Caltech 101, and DVS128 Gesture.