DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition
As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive field leads to quadratic computational cost. Another branch of Vision Transformers exploits local attention inspired by C...
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| Published in: | IEEE transactions on multimedia Vol. 25; pp. 1 - 14 |
|---|---|
| Main Authors: | , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 1520-9210, 1941-0077 |
| Online Access: | Get full text |
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| Abstract | As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive field leads to quadratic computational cost. Another branch of Vision Transformers exploits local attention inspired by CNNs, which only models the interactions between patches in small neighborhoods. Although such a solution reduces the computational cost, it naturally suffers from small attended receptive fields, which may limit the performance. In this work, we explore effective Vision Transformers to pursue a preferable trade-off between the computational complexity and size of the attended receptive field. By analyzing the patch interaction of global attention in ViTs, we observe two key properties in the shallow layers, namely locality and sparsity, indicating the redundancy of global dependency modeling in shallow layers of ViTs. Accordingly, we propose Multi-Scale Dilated Attention (MSDA) to model local and sparse patch interaction within the sliding window. With a pyramid architecture, we construct a Multi-Scale Dilated Transformer (DilateFormer) by stacking MSDA blocks at low-level stages and global multi-head self-attention blocks at high-level stages. Our experiment results show that our DilateFormer achieves state-of-the-art performance on various vision tasks. On ImageNet-1 K classification task, DilateFormer achieves comparable performance with 70% fewer FLOPs compared with existing state-of-the-art models. Our DilateFormer-Base achieves 85.6% top-1 accuracy on ImageNet-1 K classification task, 53.5% box mAP/46.1% mask mAP on COCO object detection/instance segmentation task and 51.1% MS mIoU on ADE20 K semantic segmentation task. |
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| AbstractList | As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive field leads to quadratic computational cost. Another branch of Vision Transformers exploits local attention inspired by CNNs, which only models the interactions between patches in small neighborhoods. Although such a solution reduces the computational cost, it naturally suffers from small attended receptive fields, which may limit the performance. In this work, we explore effective Vision Transformers to pursue a preferable trade-off between the computational complexity and size of the attended receptive field. By analyzing the patch interaction of global attention in ViTs, we observe two key properties in the shallow layers, namely locality and sparsity, indicating the redundancy of global dependency modeling in shallow layers of ViTs. Accordingly, we propose Multi-Scale Dilated Attention (MSDA) to model local and sparse patch interaction within the sliding window. With a pyramid architecture, we construct a Multi-Scale Dilated Transformer (DilateFormer) by stacking MSDA blocks at low-level stages and global multi-head self-attention blocks at high-level stages. Our experiment results show that our DilateFormer achieves state-of-the-art performance on various vision tasks. On ImageNet-1 K classification task, DilateFormer achieves comparable performance with 70% fewer FLOPs compared with existing state-of-the-art models. Our DilateFormer-Base achieves 85.6% top-1 accuracy on ImageNet-1 K classification task, 53.5% box mAP/46.1% mask mAP on COCO object detection/instance segmentation task and 51.1% MS mIoU on ADE20 K semantic segmentation task. As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-range dependencies between arbitrary image patches while the global attended receptive field leads to quadratic computational cost. Another branch of Vision Transformers exploits local attention inspired by CNNs, which only models the interactions between patches in small neighborhoods. Although such a solution reduces the computational cost, it naturally suffers from small attended receptive fields, which may limit the performance. In this work, we explore effective Vision Transformers to pursue a preferable trade-off between the computational complexity and size of the attended receptive field. By analyzing the patch interaction of global attention in ViTs, we observe two key properties in the shallow layers, namely locality and sparsity, indicating the redundancy of global dependency modeling in shallow layers of ViTs. Accordingly, we propose Multi-Scale Dilated Attention (MSDA) to model local and sparse patch interaction within the sliding window. With a pyramid architecture, we construct a Multi-Scale Dilated Transformer (DilateFormer) by stacking MSDA blocks at low-level stages and global multi-head self-attention blocks at high-level stages. Our experiment results show that our DilateFormer achieves state-of-the-art performance on various vision tasks. On ImageNet-1 K classification task, DilateFormer achieves comparable performance with 70% fewer FLOPs compared with existing state-of-the-art models. Our DilateFormer-Base achieves 85.6% top-1 accuracy on ImageNet-1 K classification task, 53.5% box mAP/46.1% mask mAP on COCO object detection/instance segmentation task and 51.1% MS mIoU on ADE20 K semantic segmentation task. The code is available at https://isee-ai.cn/~jiaojiayu/DilteFormer.html . |
| Author | Jiao, Jiayu Gao, Yipeng Tang, Yu-Ming Zheng, Wei-Shi Wang, Yaowei Lin, Kun-Yu Ma, Jinhua |
| Author_xml | – sequence: 1 givenname: Jiayu surname: Jiao fullname: Jiao, Jiayu organization: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China – sequence: 2 givenname: Yu-Ming surname: Tang fullname: Tang, Yu-Ming organization: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China – sequence: 3 givenname: Kun-Yu surname: Lin fullname: Lin, Kun-Yu organization: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China – sequence: 4 givenname: Yipeng surname: Gao fullname: Gao, Yipeng organization: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China – sequence: 5 givenname: Jinhua orcidid: 0000-0002-0165-8416 surname: Ma fullname: Ma, Jinhua organization: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China – sequence: 6 givenname: Yaowei orcidid: 0000-0003-2197-9038 surname: Wang fullname: Wang, Yaowei organization: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China – sequence: 7 givenname: Wei-Shi orcidid: 0000-0001-8327-0003 surname: Zheng fullname: Zheng, Wei-Shi organization: School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China |
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| Cites_doi | 10.1109/CVPR46437.2021.00729 10.1109/cvpr.2009.5206848 10.1109/iccv48922.2021.00986 10.1109/iccv48922.2021.00988 10.1007/978-3-030-58452-8_13 10.1109/iccv48922.2021.00707 10.1109/cvpr52688.2022.01181 10.1007/978-1-4899-7502-7_79-1 10.1109/tmm.2021.3086709 10.1109/tpami.2022.3206108 10.1109/cvpr.2015.7298965 10.1109/tmm.2020.2991592 10.1007/978-3-031-20053-3_27 10.1007/978-3-319-10602-1_48 10.1109/cvpr52688.2022.01186 10.1109/ICCV48922.2021.00060 10.1109/cvpr.2017.634 10.1109/iccv48922.2021.00062 10.1109/tmm.2021.3090274 10.1109/cvpr52688.2022.01167 10.1007/978-3-319-24574-4_28 10.1109/cvpr52688.2022.00714 10.1109/tmm.2021.3086758 10.1109/cvpr.2015.7298594 10.1145/3065386 10.1109/tmm.2021.3100766 10.1109/tmm.2021.3057493 10.1109/CVPR.2018.00813 10.1109/tmm.2021.3090206 10.1109/iccv48922.2021.01474 10.1609/aaai.v36i3.20252 10.1109/tmm.2022.3146775 10.1109/tmm.2020.2997192 10.1109/cvpr52688.2022.01058 10.1109/iccv.2019.00612 10.1109/tmm.2021.3050059 10.1109/iccv48922.2021.00360 10.1109/cvpr.2016.90 10.1109/tpami.2022.3164083 10.1109/cvpr.2016.308 10.1609/aaai.v34i07.7000 10.1109/ICCV48922.2021.00041 10.1109/ICCV48922.2021.00675 10.1109/CVPR42600.2020.00815 10.1109/cvpr52688.2022.00520 10.1109/iccv.2017.324 10.1109/tmm.2021.3068576 10.1109/ICCV48922.2021.00009 10.1109/CVPR42600.2020.01104 10.1109/cvpr52688.2022.01179 10.1155/2022/8917964 10.1109/iccv48922.2021.00061 10.1109/tmm.2021.3074008 10.1109/cvpr52688.2022.00943 10.1109/cvpr.2017.544 10.1007/978-3-030-01228-1_26 10.1109/CVPR42600.2020.01044 10.1109/tpami.2016.2577031 10.1109/iccv48922.2021.00010 10.1109/CVPR46437.2021.01625 10.1109/iccv48922.2021.00299 10.1109/icme52920.2022.9859907 10.1109/iccv48922.2021.00042 10.1007/978-3-319-46448-0_2 10.1109/iccv48922.2021.00044 10.1109/iccv.2017.322 10.1109/tpami.2019.2956516 10.1109/tmm.2021.3072479 10.1109/tmm.2021.3109665 10.1109/CVPR52688.2022.01055 10.1109/CVPR.2019.00656 10.1109/tmm.2020.3002614 10.1109/cvpr52688.2022.01553 10.1109/tmm.2021.3096083 10.1109/iccvw54120.2021.00301 10.1109/tmm.2021.3050082 |
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| References | ref57 ref56 ref59 ref58 ref53 ref52 ref55 ref54 Brown (ref17) 2020 Xu (ref43) 2021 Chen (ref109) 2019 Han (ref99) 2021; 34 Vaswani (ref19) 2017 ref51 Han (ref96) 2022 ref50 ref46 ref48 ref47 Li (ref103) 2022 Chu (ref24) 2021 ref42 ref41 ref49 Xiao (ref74) 2021 ref8 ref7 Yang (ref27) 2021 Contributors (ref112) 2020 ref4 ref3 ref6 ref5 ref100 ref101 ref40 ref35 ref34 ref31 ref33 Dosovitskiy (ref21) 2021 Xia (ref106) 2022 ref38 ref23 ref26 Chu (ref29) 2021 ref25 Wang (ref39) 2021 ref28 Ali (ref113) 2021 Jiang (ref32) 2021 ref13 ref12 ref15 ref11 Li (ref44) 2022 ref10 Hassani (ref94) 2022 ref98 Redmon (ref9) 2018 ref16 ref93 Radford (ref18) 2018 ref92 Loshchilov (ref105) 2019 ref95 Yu (ref36) 2021 ref91 ref86 ref85 ref88 ref87 Devlin (ref45) 2019 Dai (ref97) 2021 ref82 ref81 Yu (ref89) 2016 ref84 ref83 Cohen (ref90) 2016 ref80 Chen (ref14) 2017 ref79 ref108 ref78 ref107 ref75 ref77 ref76 ref1 Beltagy (ref20) 2020 Chen (ref69) 2022; 157 Zhou (ref104) 2021 Huang (ref37) 2021 ref71 ref111 ref70 ref73 Gildenblat (ref114) 2021 ref72 ref110 ref68 ref67 ref64 Hassani (ref30) 2022 ref63 ref66 ref65 Tan (ref102) 2019 Simonyan (ref2) 2015 ref60 Touvron (ref22) 2021 ref62 ref61 |
| References_xml | – ident: ref63 doi: 10.1109/CVPR46437.2021.00729 – ident: ref33 doi: 10.1109/cvpr.2009.5206848 – start-page: 1 volume-title: Proc. Int. Conf. Learn. Representations year: 2022 ident: ref96 article-title: On the connection between local attention and dynamic depth-wise convolution – volume: 157 start-page: 90 volume-title: Pattern Recognit. Lett. year: 2022 ident: ref69 article-title: ResT-ReID: Transformer block-based residual learning for person re-identification – start-page: 18590 volume-title: Proc. Annu. Conf. Neural Inf. Process. Syst. year: 2021 ident: ref32 article-title: All tokens matter: Token labeling for training better vision transformers – ident: ref26 doi: 10.1109/iccv48922.2021.00986 – year: 2021 ident: ref104 article-title: DeepViT: Towards deeper vision transformer publication-title: arXiv:2103.11886 – start-page: 12992 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2021 ident: ref36 article-title: Glance-and-gaze vision transformer – ident: ref52 doi: 10.1109/iccv48922.2021.00988 – year: 2022 ident: ref94 article-title: Dilated neighborhood attention transformer publication-title: arXiv:2209.15001 – year: 2022 ident: ref106 article-title: Trt-vit: TensorRT-oriented vision transformer publication-title: arXiv:2205.09579 – ident: ref61 doi: 10.1007/978-3-030-58452-8_13 – ident: ref66 doi: 10.1109/iccv48922.2021.00707 – start-page: 6105 volume-title: Proc. Int. Conf. Mach. Learn. year: 2019 ident: ref102 article-title: EfficientNet: Rethinking model scaling for convolutional neural networks – ident: ref60 doi: 10.1109/cvpr52688.2022.01181 – ident: ref47 doi: 10.1007/978-1-4899-7502-7_79-1 – ident: ref92 doi: 10.1109/tmm.2021.3086709 – ident: ref54 doi: 10.1109/tpami.2022.3206108 – start-page: 1 volume-title: Proc. 3rd Int. Conf. Learn. Representations year: 2015 ident: ref2 article-title: Very deep convolutional networks for large-scale image recognition – ident: ref15 doi: 10.1109/cvpr.2015.7298965 – start-page: 1 volume-title: Proc. 4th Int. Conf. Learn. Representations year: 2016 ident: ref89 article-title: Multi-scale context aggregation by dilated convolutions – ident: ref68 doi: 10.1109/tmm.2020.2991592 – ident: ref38 doi: 10.1007/978-3-031-20053-3_27 – ident: ref34 doi: 10.1007/978-3-319-10602-1_48 – year: 2020 ident: ref112 article-title: MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark – ident: ref75 doi: 10.1109/cvpr52688.2022.01186 – ident: ref101 doi: 10.1109/ICCV48922.2021.00060 – start-page: 1 volume-title: Proc. 7th Int. Conf. Learn. Representations year: 2019 ident: ref105 article-title: Decoupled weight decay regularization – ident: ref107 doi: 10.1109/cvpr.2017.634 – ident: ref76 doi: 10.1109/iccv48922.2021.00062 – ident: ref82 doi: 10.1109/tmm.2021.3090274 – ident: ref5 doi: 10.1109/cvpr52688.2022.01167 – start-page: 30392 volume-title: Proc. Annu. Conf. Neural Inf. Process. Syst. year: 2021 ident: ref74 article-title: Early convolutions help transformers see better – year: 2022 ident: ref30 article-title: Neighborhood attention transformer publication-title: arXiv:2204.07143 – ident: ref13 doi: 10.1007/978-3-319-24574-4_28 – year: 2021 ident: ref39 article-title: CrossFormer: A versatile vision transformer based on cross-scale attention publication-title: arXiv:2108.00154 – start-page: 2990 volume-title: Proc. 33nd Int. Conf. Mach. Learn. year: 2016 ident: ref90 article-title: Group equivariant convolutional networks – start-page: 20014 volume-title: Proc. Annu. Conf. Neural Inf. Process. Syst. year: 2021 ident: ref113 article-title: XCiT: Cross-covariance image transformers – year: 2018 ident: ref9 article-title: YOLOv3: An incremental improvement publication-title: arXiv:1804.02767 – ident: ref65 doi: 10.1109/cvpr52688.2022.00714 – year: 2020 ident: ref20 article-title: Longformer: The long-document transformer publication-title: arXiv:2004.05150 – start-page: 4171 volume-title: Proc. Conf. North Amer. Chapter Assoc. Comput. Linguistics year: 2019 ident: ref45 article-title: BERT: Pre-training of deep bidirectional transformers for language understanding – ident: ref78 doi: 10.1109/tmm.2021.3086758 – ident: ref4 doi: 10.1109/cvpr.2015.7298594 – ident: ref1 doi: 10.1145/3065386 – ident: ref85 doi: 10.1109/tmm.2021.3100766 – ident: ref83 doi: 10.1109/tmm.2021.3057493 – ident: ref16 doi: 10.1109/CVPR.2018.00813 – ident: ref84 doi: 10.1109/tmm.2021.3090206 – ident: ref70 doi: 10.1109/iccv48922.2021.01474 – ident: ref100 doi: 10.1609/aaai.v36i3.20252 – ident: ref6 doi: 10.1109/tmm.2022.3146775 – ident: ref64 doi: 10.1109/tmm.2020.2997192 – ident: ref42 doi: 10.1109/cvpr52688.2022.01058 – ident: ref48 doi: 10.1109/iccv.2019.00612 – ident: ref91 doi: 10.1109/tmm.2021.3050059 – ident: ref62 doi: 10.1109/iccv48922.2021.00360 – start-page: 28522 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2021 ident: ref43 article-title: Vitae: Vision transformer advanced by exploring intrinsic inductive bias – ident: ref3 doi: 10.1109/cvpr.2016.90 – ident: ref59 doi: 10.1109/tpami.2022.3164083 – year: 2022 ident: ref103 article-title: Next-vit: Next generation vision transformer for efficient deployment in realistic industrial scenarios publication-title: arXiv:2207.05501 – volume-title: arXiv:2107.00641 year: 2021 ident: ref27 article-title: Focal self-attention for local-global interactions in vision transformers – ident: ref49 doi: 10.1109/cvpr.2016.308 – ident: ref51 doi: 10.1609/aaai.v34i07.7000 – ident: ref88 doi: 10.1109/ICCV48922.2021.00041 – ident: ref87 doi: 10.1109/ICCV48922.2021.00675 – ident: ref50 doi: 10.1109/CVPR42600.2020.00815 – ident: ref40 doi: 10.1109/cvpr52688.2022.00520 – year: 2018 ident: ref18 article-title: Improving language understanding by generative pre-training – year: 2021 ident: ref37 article-title: Shuffle transformer: Rethinking spatial shuffle for vision transformer publication-title: arXiv:2106.03650 – ident: ref11 doi: 10.1109/iccv.2017.324 – ident: ref25 doi: 10.1109/tmm.2021.3068576 – ident: ref98 doi: 10.1109/ICCV48922.2021.00009 – ident: ref93 doi: 10.1109/CVPR42600.2020.01104 – volume: 34 start-page: 15908 volume-title: Proc. Adv. Neural Inf. Process. Syst. year: 2021 ident: ref99 article-title: Transformer in transformer – ident: ref46 doi: 10.1109/cvpr52688.2022.01179 – ident: ref71 doi: 10.1155/2022/8917964 – ident: ref31 doi: 10.1109/iccv48922.2021.00061 – year: 2019 ident: ref109 article-title: MMDetection: Open MMLab detection toolbox and benchmark publication-title: arXiv:1906.07155 – year: 2022 ident: ref44 article-title: UniFormer: Unifying convolution and self-attention for visual recognition publication-title: arXiv:2201.09450 – ident: ref58 doi: 10.1109/tmm.2021.3074008 – ident: ref80 doi: 10.1109/cvpr52688.2022.00943 – year: 2021 ident: ref24 article-title: Conditional positional encodings for vision transformers publication-title: arXiv:2102.10882 – ident: ref35 doi: 10.1109/cvpr.2017.544 – ident: ref110 doi: 10.1007/978-3-030-01228-1_26 – start-page: 9355 volume-title: Proc. Annu. Conf. Neural Inf. Process. Syst. year: 2021 ident: ref29 article-title: Twins: Revisiting the design of spatial attention in vision transformers – year: 2021 ident: ref114 article-title: Pytorch library for cam methods – ident: ref95 doi: 10.1109/CVPR42600.2020.01044 – ident: ref8 doi: 10.1109/tpami.2016.2577031 – ident: ref23 doi: 10.1109/iccv48922.2021.00010 – year: 2017 ident: ref14 article-title: Rethinking Atrous convolution for semantic image segmentation publication-title: arXiv:1706.05587 – ident: ref77 doi: 10.1109/CVPR46437.2021.01625 – ident: ref28 doi: 10.1109/iccv48922.2021.00299 – ident: ref73 doi: 10.1109/icme52920.2022.9859907 – ident: ref41 doi: 10.1109/iccv48922.2021.00042 – ident: ref10 doi: 10.1007/978-3-319-46448-0_2 – ident: ref53 doi: 10.1109/iccv48922.2021.00044 – ident: ref55 doi: 10.1609/aaai.v36i3.20252 – ident: ref12 doi: 10.1109/iccv.2017.322 – ident: ref108 doi: 10.1109/tpami.2019.2956516 – ident: ref81 doi: 10.1109/tmm.2021.3072479 – ident: ref56 doi: 10.1109/tmm.2021.3109665 – ident: ref86 doi: 10.1109/CVPR52688.2022.01055 – ident: ref111 doi: 10.1109/CVPR.2019.00656 – start-page: 10347 volume-title: Proc. 38th Int. Conf. Mach. Learn. year: 2021 ident: ref22 article-title: Training data-efficient image transformers & distillation through attention – start-page: 1 volume-title: Proc. 9th Int. Conf. Learn. Representations year: 2021 ident: ref21 article-title: An image is worth 16x16 words: Transformers for image recognition at scale – ident: ref7 doi: 10.1109/tmm.2020.3002614 – start-page: 1877 volume-title: Proc. Annu. Conf. Neural Inf. Process. Syst. year: 2020 ident: ref17 article-title: Language models are few-shot learners – ident: ref79 doi: 10.1109/cvpr52688.2022.01553 – ident: ref57 doi: 10.1109/tmm.2021.3096083 – ident: ref67 doi: 10.1109/iccvw54120.2021.00301 – ident: ref72 doi: 10.1109/tmm.2021.3050082 – start-page: 3965 volume-title: Proc. Annu. Conf. Neural Inf. Process. Syst. year: 2021 ident: ref97 article-title: Coatnet: Marrying convolution and attention for all data sizes – start-page: 5998 volume-title: Proc. Annu. Conf. Neural Inf. Process. Syst. year: 2017 ident: ref19 article-title: Attention is all you need |
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| SubjectTerms | Classification Computational efficiency Computational modeling Computing costs Convolution Object recognition Redundancy Semantic segmentation Task analysis Transformers Vision Vision Transformer |
| Title | DilateFormer: Multi-Scale Dilated Transformer for Visual Recognition |
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