基于自注意力机制的双分支密集人群计数算法
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杨天乐,李玲霞,张为
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Dual-branch crowd counting algorithm based on self-attention mechanism
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Tian-le YANG,Ling-xia LI,Wei ZHANG
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| 表 1 位置级-全监督对比实验的结果 |
| Tab.1 Results of position-level full supervision comparison experiment |
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| 算法 | SHT Part A | | SHT Part B | | UCF_QNRF | | UCF_CC_50 | | JHU-Crowd++ | | MAE | MSE | MAE | MSE | MAE | MSE | MAE | MSE | MAE | MSE | | MCNN[11] | 110.2 | 173.2 | | 26.4 | 41.3 | | 277 | 426 | | 377.6 | 509.1 | | 188.9 | 483.4 | | CSRNet[24] | 68.2 | 115.0 | 10.6 | 16.0 | — | — | 266.1 | 397.5 | 85.9 | 309.2 | | BL[25] | 62.8 | 101.8 | 7.7 | 12.7 | 88.7 | 154.8 | 229.3 | 308.2 | 75.0 | 299.9 | | DM-Count[19] | 59.7 | 95.7 | 7.4 | 11.8 | 85.0 | 148.0 | 211.0 | 291.5 | — | — | | GL[26] | 61.3 | 95.4 | 7.3 | 11.7 | 84.3 | 147.5 | — | — | 59.9 | 259.5 | | FIDT[27] | 57.0 | 103.4 | 6.9 | 11.8 | 89.0 | 153.5 | 171.4 | 233.1 | 66.6 | 253.6 | | CLTR[28] | 56.9 | 95.2 | 6.5 | 10.6 | 85.8 | 141.3 | — | — | 59.5 | 240.6 | | NDConv[29] | 61.4 | 104.18 | 7.8 | 13.8 | 91.2 | 165.6 | 167.2 | 240.6 | — | — | | DFRNet[30] | 59.6 | 100.9 | 6.9 | 12.1 | 80.2 | 145.5 | — | — | — | — | | SGANet[31] | 57.6 | 101.1 | 6.6 | 10.2 | 87.6 | 152.5 | 224.6 | 314.6 | — | — | | RAN[32] | 57.9 | 99.2 | 7.2 | 11.9 | 83.4 | 141.8 | 155.0 | 219.5 | 59.4 | 257.6 | | CTrans-MISN[33] | 55.8 | 95.9 | 7.3 | 11.4 | 95.2 | 180.1 | — | — | 71.5 | 280.1 | | DBCC-Net | 55.3 | 93.1 | 6.7 | 9.8 | 82.9 | 145.1 | 147.5 | 205.1 | 55.7 | 248.0 |
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