Papers › Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
Exploring Spatial Significance via Hybrid Pyramidal Graph Network for Vehicle Re-identification
Fei Shen, Jianqing Zhu, Xiaobin Zhu, Yi Xie, Jingchang Huang
Existing vehicle re-identification methods commonly use spatial pooling operations to aggregate feature maps extracted via off-the-shelf backbone networks. They ignore exploring the spatial significance of feature maps, eventually degrading the vehicle re-identification performance. In this paper, firstly, an innovative spatial graph network (SGN) is proposed to elaborately explore the spatial significance of feature maps. The SGN stacks multiple spatial graphs (SGs). Each SG assigns feature map's elements as nodes and utilizes spatial neighborhood relationships to determine edges among nodes. During the SGN's propagation, each node and its spatial neighbors on an SG are aggregated to the next SG. On the next SG, each aggregated node is re-weighted with a learnable parameter to find the significance at the corresponding location. Secondly, a novel pyramidal graph network (PGN) is designed to comprehensively explore the spatial significance of feature maps at multiple scales. The PGN organizes multiple SGNs in a pyramidal manner and makes each SGN handles feature maps of a specific scale. Finally, a hybrid pyramidal graph network (HPGN) is developed by embedding the PGN behind a ResNet-50 based backbone network. Extensive experiments on three large scale vehicle databases (i.e., VeRi776, VehicleID, and VeRi-Wild) demonstrate that the proposed HPGN is superior to state-of-the-art vehicle re-identification approaches.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Vehicle Re-Identification | VeRi-776 | HPGN | Rank1 | 96.72 | #13 of 17 | Archive leaderboard | report |
| Vehicle Re-Identification | VeRi-776 | HPGN | mAP | 80.18 | #13 of 17 | Archive leaderboard | report |
| Vehicle Re-Identification | VehicleID Small | HPGN | Rank1 | 83.91 | #12 of 13 | Archive leaderboard | report |
| Vehicle Re-Identification | VehicleID Small | HPGN | mAP | 89.6 | #12 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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