用大核注意力提升车辆重识别准确率
LKA-ReID:Vehicle Re-Identification with Large Kernel Attention
- 引入大核注意力机制，融合自注意力与卷积优势
- 在VeRi-776上达到86.65% mAP和98.03% Rank-1
- 适合关注车辆重识别与注意力机制应用的研究者
随着智能交通系统和智慧城市基础设施的快速发展，车辆重识别（Vehicle Re-ID）技术已成为重要研究方向。该任务面临的核心挑战是不同车辆间高度相似。现有方法依赖额外检测或分割模型提取局部差异化特征，但需额外标注或显著增加计算开销。利用注意力机制捕捉全局与局部特征对缓解类别间高相似性问题至关重要。本文提出LKA-ReID，采用大核注意力（LKA）机制，兼具自注意力与卷积的优势，更全面地提取车辆的全局与局部特征。同时引入混合通道注意力（HCA），结合通道与空间信息，使模型能更好聚焦关键通道与特征区域，抑制背景等干扰信息。在VeRi-776数据集上的实验表明，LKA-ReID有效，mAP达86.65%，Rank-1为98.03%。
原文摘要 · Abstract (English)
With the rapid development of intelligent transportation systems and the popularity of smart city infrastructure, Vehicle Re-ID technology has become an important research field. The vehicle Re-ID task faces an important challenge, which is the high similarity between different vehicles. Existing methods use additional detection or segmentation models to extract differentiated local features. However, these methods either rely on additional annotations or greatly increase the computational cost. Using attention mechanism to capture global and local features is crucial to solve the challenge of high similarity between classes in vehicle Re-ID tasks. In this paper, we propose LKA-ReID with large kernel attention. Specifically, the large kernel attention (LKA) utilizes the advantages of self-attention and also benefits from the advantages of convolution, which can extract the global and local features of the vehicle more comprehensively. We also introduce hybrid channel attention (HCA) combines channel attention with spatial information, so that the model can better focus on channels and feature regions, and ignore background and other disturbing information. Experiments on VeRi-776 dataset demonstrated the effectiveness of LKA-ReID, with mAP reaches 86.65% and Rank-1 reaches 98.03%.
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