# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 2 Related Works
### Methods Based on Attention Mechanisms  
Attention mechanisms, inspired by selective attention in human vision, have been extensively adopted in computer vision to enhance feature representation and improve model performance. Channel attention mechanisms have focused on adaptively weighting feature channels to emphasize informative features while suppressing irrelevant ones. Early approaches introduced fully connected networks to recalibrate channel-wise responses, strengthening key information extraction [30]. Further refinements explored inter-channel relationships using normalization techniques, balancing competition and cooperation among channels through global context embedding and gated adaptation, thereby enhancing the network's capacity to capture essential features [31]. Spatial attention mechanisms,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline
**VATT**[26] & Convolution-free, unified multimodal learning & High computational cost, lacks fine-grained feature localization \\ \hline
**EM-Trans**[27] & Dual-band decomposition, improves saliency detection & Limited to saliency tasks, complex fusion design \\ \hline
**ETFormer**[28] & Single encoder for RGB-D-T fusion, optimized with multimodal attention & Requires large datasets, high model complexity \\ \hline
**HCANet**[29] & Global–local fusion for hyperspectral denoising, multiscale extraction & Task-specific, depends on specialized loss functions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different multimodal Transformer methods.  
on the other hand, have been designed to improve adaptability to geometric variations by dynamically transforming spatial features. Parameterized sampling grids and localized transformation modules have been employed to enable flexible spatial manipulations of feature maps, allowing networks to adjust to spatial deformations more effectively [32]. Meanwhile, hybrid attention approaches have integrated both channel and spatial attention to refine feature representations further. A common strategy involves sequentially inferring channel and spatial attention maps to achieve more comprehensive feature enhancement, leveraging multiscale feature extraction and cross-dimensional weighting for improved fusion of local and global contextual information [33; 34]. Recent advancements have extended attention mechanisms beyond conventional applications, incorporating hybrid strategies that fuse global and local features while enhancing multiscale feature extraction. These improvements have proven effective in tasks such as hyperspectral image denoising, where targeted loss functions and feature calibration techniques contribute to better noise suppression and feature preservation [29].  
In this study, partial attention mechanisms are integrated to strengthen unimodal feature representations, enhancing the robustness and accuracy of multimodal fusion and target detection. By selectively emphasizing critical features across modalities, the proposed approach improves detection performance in complex environments. Various attention mechanisms and their characteristics are summarized in Table 2.