基于Transformer的高效自适应语义分割网络
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张海波,蔡磊,任俊平,王汝言,刘富
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Efficient and adaptive semantic segmentation network based on Transformer
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Hai-bo ZHANG,Lei CAI,Jun-ping REN,Ru-yan WANG,Fu LIU
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| 表 1 不同分割模型在ADE20K数据集上的模型评估结果 |
| Tab.1 Model evaluation results of different segmentation models on ADE20K dataset |
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| 算法 | 基础网络结构 | N/106 | GFLOPs | mIoU/
% | | FCN[1] | ResNet-101[19] | 68.6 | 275.7 | 39.9 | | PSPNet[3] | ResNet-101 | 68.1 | 256.4 | 44.3 | | DeepLab-V3+[7] | ResNet-101 | 62.7 | 255.1 | 45.4 | | DeepLab-V3+ | ResNeSt-101[21] | 66.3 | 262.9 | 46.9 | | UperNet[22] | DeiT[23] | 120.5 | 90.1 | 45.3 | | UperNet | Swin-S[24] | 81.0 | 259.3 | 49.3 | | UperNet | Convnext[25] | 60.2 | 234.6 | 46.1 | | UperNet | Focal-B[26] | 126.0 | — | 49.0 | | SETR[11] | ViT[12] | 318.5 | 213.6 | 47.3 | | DPT[27] | ViT | 109.7 | 171.0 | 46.9 | | Segmenter Mask[28] | ViT | 102.5 | 71.1 | 49.6 | | Semantic FPN[29] | PVTv2-B3[30] | 49.0 | 62.0 | 47.3 | | Semantic FPN | VAN-B3[31] | 49.0 | 68.0 | 48.1 | | SeMask-B FPN[32] | SeMask Swin | 96.0 | 107.0 | 49.4 | | Segformer[20] | MiT[20] | 83.9 | 110.5 | 50.1 | | EA-Former | MiT | 136.4 | 61.3 | 49.3 | | Segformer* | MiT | 83.9 | 172.7 | 52.1 | | EA-Former* | MiT | 136.4 | 95.8 | 51.0 |
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