Graph-Based Vehicle Keypoint Attention Model for Vehicle Re-identification

Vehicle re-identification is the task of locating a particular vehicle image among a set of images of vehicles captured from different cameras. In recent years, many methods focus on learning distinctive global features by incorporating keypoint details to improve re-identification accuracy. However, these methods do not take into account the relation between different keypoints and the relation between keypoints and the overall vehicle. To address this limitation, we propose the Graph-based Vehicle Keypoint Attention (GVKA) model that integrates keypoint features and two relation components to yield robust and discriminative representations of vehicle images. The model extracts keypoint features using a pre-trained model, models the relation among keypoint features using a Graph Convolutional Network, and employs cross-attention to highlight important areas of the vehicle and establish the relation between keypoint features and the overall vehicle. Our experimental results on three large-scale datasets demonstrate the effectiveness of our proposed method.