基于注意力和自适应权重的车辆重识别算法
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苏育挺,陆荣烜,张为
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Vehicle re-identification algorithm based on attention mechanism and adaptive weight
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Yu-ting SU,Rong-xuan LU,Wei ZHANG
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| 表 5 VehicleID数据集下与主流算法的结果对比 |
| Tab.5 Comparison of results with mainstream algorithms in VehicleID datasets |
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| % | | 方法 | 小尺度 | | 中尺度 | | 大尺度 | | Rank-1 | mAP | Rank-1 | mAP | Rank-1 | mAP | | OIFE[19] | — | — | | — | — | | 67.0 | — | | VAMI[20] | 63.1 | — | 52.9 | — | 47.3 | — | | PNVR[21] | 78.4 | — | 75.0 | — | 74.2 | — | | MRL[22] | 75.7 | 78.3 | 71.6 | 75.4 | 66.5 | 68.2 | | UMTS[6] | 74.4 | 80.4 | 72.4 | 77.1 | 69.8 | 75.2 | | PVEN[23] | 78.4 | 78.3 | 75.0 | 78.3 | 74.2 | 78.3 | | TBE[24] | 86.0 | — | 82.3 | — | 80.7 | — | | 本文方法 | 84.5 | 85.3 | 82.5 | 82.6 | 81.5 | 80.9 |
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