# A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification
## 5 Discussion  
In this study, we proposed a novel module that harnesses the graph attention mechanism to aggregate human feature information. After analyzing other transformer-based models, we believe that the self-attention mechanism in ViT might be vulnerable to interference caused by occluding objects, which can result in attention being scattered across irrelevant areas. To mitigate the impact of occlusions, some approaches have utilized pose estimation or human key point detection to create graph-structured data. However, these methods introduce additional noise, partly due to challenges like the invisibility of key points. Our method addresses the reliance of graph-based models on human key point detection through reassembling the features extracted with ViT to concentrate on effective information segments under occlusion scenarios. Our overarching framework employs ViT as the backbone, treating features extracted by ViT as graph nodes, which are then processed with our multi-scale attention module for information aggregation. By adopting graph structures and attention-driven feature learning, the network is encouraged to focus on features that persist stably, even under occluded conditions, while integrating pedestrian features across various scales. This approach sustains robust recognition performance in the presence of occlusions. Through assigning attention weights to feature maps at different scales, the model effectively integrates both global and local information. Our MSGA-ReID surpasses most existing models based on graph convolutional approaches--albeit not outperforming the most recent ones--which remains an area for future investigation and improvement. Currently, our method for constructing graph-structured information relies solely on feature similarity, which may not fully align with the feature distribution of person re-identification tasks, thus potentially degrading model performance. We found that existing ReID models based on graph structures, include ours, have some limitations when dealing with changes in occlusion patterns. Specifically, the current dataset size is not sufficient to cover all population and environmental changes, resulting in limited generalization ability of the model on new samples. In addition, in terms of occlusion processing, the model may have difficulty effectively dealing with situations where the occlusion area is large or the occlusion position is uncertain. To overcome these limitations, future directions should include expanding the dataset size, developing more robust occlusion processing strategies, and improving the model's cross-domain adaptability. We plan to enhance the robustness of the model by designing new occlusion data augmentation methods and utilizing multi-scale feature representation and attention mechanisms. We will also explore efficient graph processing algorithms and techniques to reduce computational costs and enable better deployment of models in practical applications. We plan to explore these issues in future work. This research received no external funding. Not applicable. Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found here: [[https://github.com/lightas/Occluded-DukeMTMC-Dataset](https://github.com/lightas/Occluded-DukeMTMC-Dataset)]([https://github.com/lightas/Occluded-DukeMTMC-Dataset](https://github.com/lightas/Occluded-DukeMTMC-Dataset)); [[https://github.com/tinajia2012/ICME2018_Occluded-Person-Reidentification_datasets](https://github.com/tinajia2012/ICME2018_Occluded-Person-Reidentification_datasets)]([https://github.com/tinajia2012/ICME2018_Occluded-Person-Reidentification_datasets](https://github.com/tinajia2012/ICME2018_Occluded-Person-Reidentification_datasets)); [[https://hyper.ai/datasets/16904](https://hyper.ai/datasets/16904)]([https://hyper.ai/datasets/16904](https://hyper.ai/datasets/16904)) (accessed on 11 September 2024). The authors acknowledge the anonymous reviewers for their helpful comments on the manuscript.