基于自注意力机制的双分支密集人群计数算法

杨天乐,李玲霞,张为

Dual-branch crowd counting algorithm based on self-attention mechanism

Tian-le YANG,Ling-xia LI,Wei ZHANG

表 3 时间及空间复杂度对比的实验结果

Tab.3 Experimental results of time and space complexity comparison

类型 算法 框架方式 MAE MSE Np/106 GFLOPs/109 全监督 CAN[38] CNN 62.3 100.0 18.1 64.6 FIDT[27] CNN 57.0 103.4 66.6 80.1 RAN[32] CNN 57.9 99.2 22.9 115.9 DBCC-Net CNN+Transformer 55.3 93.1 38.0 98.5 弱监督 TransCrowd[16] Transformer 66.1 95.4 86.0 49.3 CCTrans[39] Transformer 64.4 95.4 104.0 57.6 CCST[36] Transformer 62.8 94.1 294.7 323.6 DBCC-Net CNN+Transformer 60.1 94.0 38.0 98.5