# Multispectral Target Detection Based on Deep Feature Fusion of Visible and Infrared Modalities
## 2 Related Works
### Transformer for Multimodal Learning  
Transformer models have emerged as a powerful tool in multimodal learning, demonstrating remarkable scalability and adaptability across various modalities, including speech, vision, and hearing. Their success in diverse tasks such as language translation, image recognition, and speech processing can be attributed to their reduced dependence on  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline
**SENet**[30] & Enhances key feature channels through adaptive recalibration techniques & Limited to channel-wise attention, lacks spatial adaptability \\ \hline
**GCT**[31] & Regulates channel competition and cooperation, improving feature expressiveness & Computationally expensive due to normalization and gating mechanisms \\ \hline
**Spatial Transformer**[32] & Enables flexible spatial transformations, improving adaptability to geometric variations & Requires additional computational overhead for transformation estimation \\ \hline
**CBAM**[33] & Sequentially applies channel and spatial attention for enhanced feature representation & Increased model complexity due to multiple attention stages \\ \hline
**EPSANet**[34] & Leverages multiscale attention fusion, effectively integrating local and global information & High computational cost, challenging to optimize \\ \hline
**HCANet**[29] & Combines global and local features for hyperspectral image denoising & Task-specific, less generalizable to other vision tasks \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of attention mechanisms.  
modality-specific architectural constraints, such as translational invariance or local grid attention biases [35]. Unlike conventional architectures, Transformers process input as token sequences, allowing for flexible modeling of both intra-modal feature extraction and inter-modal correlation learning through controlled self-attention mechanisms [36]. Building on this foundation, recent studies have explored Transformer-based multimodal fusion strategies to enhance representation learning. Convolution-free architectures have been employed to jointly learn audio, video, and text modalities, leveraging modal signal preprocessing, feature extraction, and contrastive learning to improve cross-modal alignment [26]. Further advancements introduced decomposition and parallel decoding techniques, enabling the extraction of edge and subject features while predicting saliency maps through a sequence of fusion modules, leading to state-of-the-art performance in RGB-D salient target detection [27]. Expanding this approach to tri-modal data, unified Transformer models have integrated multimodal attention mechanisms to refine feature fusion, benefiting from large-scale pretraining and optimization of multiple loss functions to enhance detection performance [28].  
These studies collectively validate the effectiveness of Transformer models in multimodal learning. To further exploit their ability to capture global contextual features and leverage complementary information between RGB and thermal imaging, this paper integrates the Transformer framework into multispectral target detection. By providing global semantic support for cross-modal feature fusion, this approach enhances target detection accuracy and overall model robustness in complex environments. A summary of representative Transformer-based multimodal learning methods is provided in Table 3.