Key Problems and Progress of Vision Transformers: The State of the Art and Prospects
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摘要: Transformer所具备的长距离建模能力和并行计算能力使其在自然语言处理领域取得了巨大成功并逐步拓展至计算机视觉等领域. 本文以分类任务为切入, 介绍了典型视觉Transformer的基本原理和结构, 并分析了Transformer与卷积神经网络在连接范围、权重动态性和位置表示能力三方面的区别与联系; 同时围绕计算代价、性能提升、训练优化以及结构设计四个方面总结了视觉Transformer研究中的关键问题以及研究进展; 并提出了视觉Transformer的一般性框架; 然后针对检测和分割两个领域, 介绍了视觉Transformer在特征学习、结果产生和真值分配等方面给上层视觉模型设计带来的启发和改变; 并对视觉Transformer未来发展方向进行了展望.
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关键词:
- 视觉Transformer /
- 图像分类 /
- 目标检测 /
- 图像分割 /
- 计算机视觉
Abstract: Due to its long-range sequence modeling and parallel computing capability, Transformers have achieved significant success in natural language processing and are gradually expanding to computer vision area. Starting from image classification, we introduce the architecture of classic vision Transformer and compare it with convolutional neural networks in connection range, dynamic weights and position representation ability. Then, we summarize existing problems and corresponding solutions in vision Transformers including computational efficiency, performance improvement, optimization and architecture design. Besides, we propose a general architecture of Vision Transformers. For object detection and image segmentation, we discuss Transformer-based models and their roles on feature extraction, result generation and ground-truth assignment. Finally, we point out the development trends of vision Transformers.-
Key words:
- Vision Transformers /
- image classification /
- object detection /
- image segmentation /
- computer vision
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图 4 单尺度与多尺度结构对比
Fig. 4 The comparison of single-scale framework and multi-scale framework
图 5 类Transformer方法的交互模块结构对比(Transformer[8], MLP-Mixer[85], ResMLP[86], gMLP[87], CycleMLP[88], ConvMixer[89], PoolFormer[90], MetaFormer[90], CMT[34], CVX[91])
Fig. 5 The comparison of mixing blocks of Transformer-like methods (Transformer[8], MLP-Mixer[85], ResMLP[86], gMLP[87], CycleMLP[88], ConvMixer[89], PoolFormer[90], MetaFormer[90], CMT[34], CVX[91])
图 8 基于Transformer和CNN的目标检测器的收敛速度对比(DETR-DC5[16], TSP-FCOS[115], TSP-RCNN[115], SMCA[110], Deformable DETR[24], Conditional DETR-DC5-R50[111], RetinaNet[104], Faster R-CNN[95], Sparse R-CNN[108])
Fig. 8 The comparison of converge speed among object detectors based on Transformer and CNN (DETR-DC5[16], TSP-FCOS[115], TSP-RCNN[115], SMCA[110], Deformable DETR[24], Conditional DETR-DC5-R50[111], RetinaNet[104], Faster R-CNN[95], Sparse R-CNN[108])
表 1 不同Transformer自注意力机制以及卷积的时间和空间复杂度(
$ N $ ,$ d $ ,$ s $ 分别表示序列长度、特征维度和局部窗口尺寸, 其中$s<N$ )Table 1 The time and space complexity of different Transformer frameworks (
$N$ ,$ d $ ,$ s $ denote the length, dimension and local window size respectively)名称 时间复杂度 空间复杂度 Convolution $ {\rm{O}}(Nd^2s) $ $ {\rm{O}}(Ns^2d^2+Nd) $ Transformer[8] $ {\rm{O}}(N^2d) $ $ {\rm{O}}(N^2+Nd) $ Sparse Transformers[49] $ {\rm{O}}(N\sqrt{N}d) $ — Reformer[50] $ {\rm{O}}(N\log Nd) $ $ {\rm{O}}(N\log N+Ld) $ Linear Transformer[33] $ {\rm{O}}(Nd^2) $ $ {\rm{O}}(Nd+d^2) $ Performer[54] $ {\rm{O}}(Nd^2\log d) $ $ {\rm{O}}(Nd\log d+d^2\log d) $ AFT-simple[56] $ {\rm{O}}(Nd) $ $ {\rm{O}}(Nd) $ AFT-full[56] $ {\rm{O}}(N^2d) $ $ {\rm{O}}(Nd) $ AFT-local (1D)[56] $ {\rm{O}}(Nsd) $ $ {\rm{O}}(Nd) $ Swin Transformer (2D)[23] ${\rm{O}}(Ns^2d)$ — 
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表 2 视觉Transformer算法在ImageNet-1k上的Top-1准确率比较
Table 2 The comparison of Top-1 accuracy of different vision Transformers on ImageNet-1k dataset
方法名称 迭代轮次 批处理大小 参数量 (×106) 计算量 (GFLOPs) 图像尺寸 Top-1 准确率 训练 测试 ViT-B/16[15] 300 4 096 86 743 224 384 77.9 ViT-L/16[15] 307 5172 224 384 76.5 DeiT-Ti[58] 300 1 024 5 1.3 224 224 72.2 DeiT-S[58] 22 4.6 224 224 79.8 DeiT-B[58] 86 17.6 224 224 81.8 DeiT-B$ \uparrow $[58] 86 52.8 224 384 83.1 ConViT-Ti[60] 300 512 6 1 224 224 73.1 ConViT-S[60] 27 5.4 224 224 81.3 ConViT-B[60] 86 17 224 224 82.4 LocalViT-T[61] 300 1 024 5.9 1.3 224 224 74.8 LocalViT-S[61] 22.4 4.6 224 224 80.8 CeiT-T[73] 300 1 024 6.4 1.2 224 224 76.4 CeiT-S[73] 24.2 4.5 224 224 82.0 CeiT-S$ \uparrow $[73] 24.2 12.9 224 384 83.3 ResT-Small[53] 300 2 048 13.66 1.9 224 224 79.6 ResT-Base[53] 30.28 4.3 224 224 81.6 ResT-Large[53] 51.63 7.9 224 224 83.6 Swin-T[23] 300 1 024 29 4.5 224 224 81.3 Swin-S[23] 50 8.7 224 224 83.0 Swin-B[23] 88 15.4 224 224 83.3 Swin-B$ \uparrow $[23] 88 47.0 224 384 84.2 VOLO-D1[68] 300 1 024 27 6.8 224 224 84.2 VOLO-D2[68] 59 14.1 224 224 85.2 VOLO-D3[68] 86 20.6 224 224 85.4 VOLO-D4[68] 193 43.8 224 224 85.7 VOLO-D5[68] 296 69.0 224 224 86.1 VOLO-D5$ \uparrow $[68] 296 304 224 448 87.0 PVT-Tiny[22] 300 128 13.2 1.9 224 224 75.1 PVT-Small[22] 24.5 3.8 224 224 79.8 PVT-Medium[22] 44.2 6.7 224 224 81.2 PVT-Large[22] 61.4 9.8 224 224 81.7 DeepViT-S[66] 300 256 27 6.2 224 224 82.3 DeepViT-L[66] 55 12.5 224 224 83.1 Refined-ViT-S[59] 300 256 25 7.2 224 224 83.6 Refined-ViT-M[59] 55 13.5 224 224 84.6 Refined-ViT-L[59] 81 19.1 224 224 84.9 Refined-ViT-L$ \uparrow $[59] 512 81 69.1 224 384 85.7 CrossViT-9[63] 300 4 096 8.6 1.8 224 224 73.9 CrossViT-15[63] 27.4 5.8 224 224 81.5 CrossViT-18[63] 43.3 9.0 224 224 82.5
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表 3 基于Transformer和基于CNN的目标检测算法在COCO 2017 val数据集上的检测精度比较. 其中C. 表示基于CNN的算法, T. 表示基于Transformer的算法
Table 3 The comparison of detection performance of Transformer-based and CNN-based detectors on COCO 2017 val set. C. denotes CNN-based methods, T. denotes Transformer-based methods
类型 方法名称 迭代轮次 计算量 (GFLOPs) 参数量 (×106) 帧数 (FPS) 多尺度输入 $ AP $ $ AP_{50} $ $ AP_{75} $ $ AP_{S} $ $ AP_{M} $ $ AP_{L} $ C. FCOS[116] 36 177 — 17 √ 41.0 59.8 44.1 26.2 44.6 52.2 Faster R-CNN[95] 36 180 42 26 √ 40.2 61.0 43.8 24.2 43.5 52.0 Faster R-CNN+[95] 108 180 42 26 √ 42.0 62.1 45.5 26.6 45.4 53.4 Mask R-CNN[99] 36 260 44 — √ 41.0 61.7 44.9 — — — Cascade Mask R-CNN[105] 36 739 82 18 √ 46.3 64.3 50.5 — — — T. ViT-B/16-FRCNN$ \ddagger $[117] 21 — — — — 36.6 56.3 39.3 17.4 40.0 55.5 ViT-B/16-FRCNN*[117] 21 — — — — 37.8 57.4 40.1 17.8 41.4 57.3 DETR-R50[16] 500 86 41 28 — 42.0 62.4 44.2 20.5 45.8 61.1 DETR-DC5-R50[16] 500 187 41 12 — 43.3 63.1 45.9 22.5 47.3 61.1 ACT-MTKD (L=16)[113] — 156 — 14 — 40.6 — — 18.5 44.3 59.7 ACT-MTKD (L=32)[113] — 169 — 16 — 43.1 — — 22.2 47.1 61.4 Deformable DETR[24] 50 78 34 27 — 39.7 60.1 42.4 21.2 44.3 56.0 Deformable DETR-DC5[24] 50 128 34 22 — 41.5 61.8 44.9 24.1 45.3 56.0 Deformable DETR[24] 50 173 40 19 √ 43.8 62.6 47.7 26.4 47.1 58.0 Two-Stage Deformable DETR[24] 50 173 40 19 √ 46.2 65.2 50.0 28.8 49.2 61.7 SMCA[110] 50 152 40 22 — 41.0 — — 21.9 44.3 59.1 SMCA+[110] 108 152 40 22 — 42.7 — — 22.8 46.1 60.0 SMCA[110] 50 152 40 10 √ 43.7 63.6 47.2 24.2 47.0 60.4 SMCA+[110] 108 152 40 10 √ 45.6 65.5 49.1 25.9 49.3 62.6 Efficient DETR[109] 36 159 32 — √ 44.2 62.2 48.0 28.4 47.5 56.6 Efficient DETR*[109] 36 210 35 — √ 45.1 63.1 49.1 28.3 48.4 59.0 Conditional DETR[111] 108 90 44 — — 43.0 64.0 45.7 22.7 46.7 61.5 Conditional DETR-DC5[111] 108 195 44 — — 45.1 65.4 48.5 25.3 49.0 62.2 UP-DETR[112] 150 86 41 28 — 40.5 60.8 42.6 19.0 44.4 60.0 UP-DETR+[112] 300 86 41 28 — 42.8 63.0 45.3 20.8 47.1 61.7 TSP-FCOS[115] 36 189 51.5 15 √ 43.1 62.3 47.0 26.6 46.8 55.9 TSP-RCNN[115] 36 188 64 11 √ 43.8 63.3 48.3 28.6 46.9 55.7 TSP-RCNN+[115] 96 188 64 11 √ 45.0 64.5 49.6 29.7 47.7 58.0 YOLOS-S[114] 150 200 30.7 7 — 36.1 56.4 37.1 15.3 38.5 56.1 YOLOS-S[114] 150 179 27.9 5 √ 37.6 57.6 39.2 15.9 40.2 57.3 YOLOS-B[114] 150 537 127 — — 42.0 62.2 44.5 19.5 45.3 62.1
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表 4 基于Transformer的语义分割算法在ADE20K val数据集上的语义分割精度比较. 其中, 1k表示ImageNet-1k, 22k表示ImageNet-1k和ImageNet-21k的结合
Table 4 The comparison of semantic segmentation performance of Transformer-based methods on ADE20K val set. 1k denotes ImageNet-1k dataset, 22k denotes the combination of ImageNet-1k and ImageNet-21k
方法名称 骨干网络 预训练数据集 图像尺寸 参数量 (×106) 计算量 (GFLOPs) 帧数 (FPS) 多尺度输入 mIoU UperNet[122] R-50 1k 512 — — 23.4 √ 42.8 R-101 1k 512 86 1 029 20.3 √ 44.9 Swin-T 1k 512 60 236 18.5 √ 46.1 Swin-S 1k 512 81 259 15.2 √ 49.3 Swin-B 22k 640 121 471 8.7 √ 51.6 Swin-L 22k 640 234 647 6.2 √ 53.5 Segformer[25] MiT-B3 1k 512 47.3 79 — √ 50.0 MiT-B4 1k 512 64.1 95.7 15.4 √ 51.1 MiT-B5 1k 512 84.7 183.3 9.8 √ 51.8 Segmenter[124] ViT-S/16 1k 512 37.0 — 34.8 √ 46.9 ViT-B/16 1k 512 106 — 24.1 √ 50.0 ViT-L/16 22k 640 334 — — √ 53.6 MaskFormer[125] R-50 1k 512 41 53 24.5 √ 46.7 R-101 1k 512 60 73 19.5 √ 47.2 Swin-T 1k 512 42 55 22.1 √ 48.8 Swin-S 1k 512 63 79 19.6 √ 51.0 Swin-B 22k 640 102 195 12.6 √ 53.9 Swin-L 22k 640 212 375 7.9 √ 55.6 Mask2Former[26] R-50 1k 512 — — — √ 49.2 R-101 1k 512 — — — √ 50.1 Swin-S 1k 512 — — — √ 52.4 Swin-B 22k 640 — √ 55.1 Swin-L 22k 640 — — — √ 57.3
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表 5 基于Transformer的实例分割方法和基于CNN算法在COCO test-dev数据集上的实例分割精度比较
Table 5 The comparison of instance segmentation performance of Transformer-based and typical CNN-based methods on COCO test-dev dataset
方法名称 骨干网络 迭代轮次 帧数 (FPS) $ Ap^m $ $ Ap_S^m $ $ Ap_M^m $ $ Ap_L^m $ $ Ap^b $ Mask R-CNN[99] R-50-FPN 36 15.3 37.5 21.1 39.6 48.3 41.3 R-101-FPN 36 11.8 38.8 21.8 41.4 50.5 43.1 Blend Mask[96] R-50-FPN 36 15.0 37.8 18.8 40.9 53.6 43.0 R-101-FPN 36 11.5 39.6 22.4 42.2 51.4 44.7 SOLO v2[97] R-50-FPN 36 10.5 38.2 16.0 41.2 55.4 40.7 R-101-FPN 36 9.0 39.7 17.3 42.9 57.4 42.6 ISTR[127] R-50-FPN 36 13.8 38.6 22.1 40.4 50.6 46.8 R-101-FPN 36 11.0 39.9 22.8 41.9 52.3 48.1 SOLQ[98] R-50 50 — 39.7 21.5 42.5 53.1 47.8 R-101 50 — 40.9 22.5 43.8 54.6 48.7 Swin-L 50 — 45.9 27.8 49.3 60.5 55.4 QueryInst[126] R-50-FPN 36 7.0 40.6 23.4 42.5 52.8 45.6 R-101-FPN 36 6.1 41.7 24.2 43.9 53.9 47.0 Swin-L 50 3.3 49.1 31.5 51.8 63.2 56.1 Mask2Former[26] R-50 50 — 43.7 — — — 30.6 R-101 50 — 44.2 — — — 31.1 Swin-T 50 — 45.0 — — — 31.8 Swin-L 50 — 50.1 — — — 36.2
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表 6 基于Transformer的全景分割算法在COCO panoptic minval数据集上的全景分割精度比较
Table 6 The comparison of panoptic segmentation performance of Transformer-based methods on COCO panoptic minival dataset
方法名称 骨干网络 迭代轮次 参数量 (×106) 计算量 (GFLOPs) $ PQ $ $ PQ^{Th} $ $ PQ^{St} $ DETR[16] R-50 150+25 42.8 137 43.4 48.2 36.3 R-101 61.8 157 45.1 50.5 37 MaxDeepLab[123] Max-S 54 61.9 162 48.4 53.0 41.5 Max-L 451 1 846 51.1 57.0 42.2 MaskFormer[125] R-50 300 45 181 46.5 51.0 39.8 R-101 64 248 47.6 52.5 40.3 Swin-T 42 179 47.7 51.7 41.7 Swin-S 63 259 49.7 54.4 42.6 Swin-B 102 411 51.1 56.3 43.2 Swin-L 212 792 52.7 58.5 44.0 Panoptic SegFormer[128] R-50 12 51.0 214 48.0 52.3 41.5 R-50 24 51.0 214 49.6 54.4 42.4 R-101 69.9 286 50.6 55.5 43.2 Swin-L 221.4 816 55.8 61.7 46.9
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