# A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification
###### Abstract  
The objective of person re-identification (ReID) tasks is to match a specific individual across different times, locations, or camera viewpoints. The prevalent issue of occlusion in real-world scenarios affects image information, rendering the affected features unreliable. The difficulty and core challenge lie in how to effectively discern and extract visual features from human images under various complex conditions, including cluttered backgrounds, diverse postures, and the presence of occlusions. Some works have employed pose estimation or human key point detection to construct graph-structured information to counteract the effects of occlusions. However, this approach introduces new noise due to issues such as the invisibility of key points. Our proposed module, in contrast, does not require the use of additional feature extractors. Our module employs multi-scale graph attention for the reweighting of feature importance. This allows features to concentrate on areas genuinely pertinent to the re-identification task, thereby enhancing the model's robustness against occlusions. To address these problems, a model that employs multi-scale graph attention to reweight the importance of features is proposed in this study, significantly enhancing the model's robustness against occlusions. Our experimental results demonstrate that, compared to baseline models, the method proposed herein achieves a notable improvement in mAP on occluded datasets, with increases of 0.5%, 31.5%, and 12.3% in mAP scores. person re-identification; graph attention; occluded; GCN +
Footnote †: journal: Applied  
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misdirected towards irrelevant areas. Most graph-based works [11; 12; 13; 14; 15; 16; 17; 18; 19] have used human key points as graph nodes to integrate key point information. Therefore, corresponding modules must be used to extract human body node information or perform pose estimation. However, the proportion of human content in features is unpredictable. In the case of occluded datasets in particular, considering the influence of occluded objects on human features, missing key point information may weaken the robustness of the model. To address the above limitation, we introduce a novel module that employs multi-scale graph attention for feature weight redistribution. Our module does not need to extract human body node information, automatically encodes the basic features extracted with Vision Transformer (ViT) into the graph structure, and carries out multi-scale graph attention [20; 21] aggregation to solve the problem of the imbalance of the human body image in the occlusion scene. The results of visualization experiments show that our module can refocus the ViT-extracted features on more important parts. Building upon this innovation, we propose a person re-identification model specifically designed to excel in occlusion scenarios, aiming to achieve enhanced robustness and accuracy under challenging conditions where occlusions are prevalent. In this study, a feature extraction network is introduced that combines transformers and multi-scale graph convolutional feature fusion and is specifically designed for occluded person re-identification (ReID) datasets. Our model employs Vision Transformer [22] as the backbone for image feature extraction, where images are divided into multiple patches and input into ViT. The final layer's transformer outputs are treated as nodes [5; 23], with the class token serving as the central node. Given that the class token aggregates information from all tokens, edges are established between the class token and other nodes; in comparison, additional edges are formed between other nodes based on their feature similarity, creating a graph of nodes. This graph, along with node features, is then fed into a multi-scale graph attention module featuring three branches, each utilizing a graph attention network of a different scale. Moreover, we use fully connected layers to map the features to a common dimension before concatenation.