Skip to main content

Advertisement

Springer Nature Link
Log in

MAN and CAT: mix attention to nn and concatenate attention to YOLO

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

CNNs have achieved remarkable image classification and object detection results over the past few years. Due to the locality of the convolution operation, although CNNs can extract rich features of the object itself, they can hardly obtain global context in images. It means the CNN-based network is not a good candidate for detecting objects by utilizing the information of the nearby objects, especially when the partially obscured object is hard to detect. ViTs can get a rich context and dramatically improve the prediction in complex scenes with multi-head self-attention. However, it suffers from long inference time and huge parameters, which leads ViT-based detection network that is hardly be deployed in the real-time detection system. In this paper, firstly, we design a novel plug-and-play attention module called mix attention (MA). MA combines channel, spatial and global contextual attention together. It enhances the feature representation of individuals and the correlation between multiple individuals. Secondly, we propose a backbone network based on mix attention called MANet. MANet-Base achieves the state-of-the-art performances on ImageNet and CIFAR. Last but not least, we propose a lightweight object detection network called CAT-YOLO, where we make a trade-off between precision and speed. It achieves the AP of 25.7% on COCO 2017 test-dev with only 9.17 million parameters, making it possible to deploy models containing ViT on hardware and ensure real-time detection. CAT-YOLO could better detect obscured objects than other state-of-the-art lightweight models.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the current study are available from their official websites and corresponding author on a reasonable request.

References

  1. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778

  2. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. preprint arXiv:1409.1556

  3. Tan M, Le Q (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, PMLR, pp 6105–6114

  4. Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Advances in neural information processing systems. 28

  5. Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. Preprint arXiv:1804.02767

  6. Bochkovskiy A, Wang C-Y, Liao H-YM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934

  7. Jocher G (2021) YOLOv5. https://github.com/ultralytics/yolov5

  8. Ge Z, Liu S, Wang F, Li Z, Sun J (2021) Yolox: exceeding yolo series in 2021. Preprint arXiv:2107.08430

  9. Shao R, Shi Z, Yi J, Chen P-Y, Hsieh C-J (2021) On the adversarial robustness of visual transformers. arXiv e-prints, 2103

  10. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. Preprint arXiv:2010.11929

  11. Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 10012–10022

  12. Chu X, Tian Z, Wang Y, Zhang B, Ren H, Wei X, Xia H, Shen,C (2021) Twins: revisiting the design of spatial attention in vision transformers. Advances in Neural Information Processing Systems, 34

  13. Touvron H, Cord M, Douze M, Massa F, Sablayrolles, A, Jégou H (2021) Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, PMLR, pp 10347–10357

  14. Yuan K, Guo S, Liu Z, Zhou A, Yu F, Wu W (2021) Incorporating convolution designs into visual transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 579–588

  15. Han K, Xiao A, Wu E, Guo J, Xu C, Wang Y (2021) Transformer in transformer. Advances in Neural Information Processing Systems, 34

  16. Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European Conference on Computer Vision, Springer, pp 213–229

  17. Fang Y, Liao B, Wang X, Fang J, Qi J, Wu R, Niu J, Liu W (2021) You only look at one sequence: rethinking transformer in vision through object detection. Advances in Neural Information Processing Systems, 34

  18. Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable detr: deformable transformers for end-to-end object detection. Preprint arXiv:2010.04159

  19. Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7132–7141

  20. Li X, Wang W, Hu X, Yang J (2019) Selective kernel networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 510–519

  21. Woo S, Park J, Lee J-Y, Kweon IS (2018) Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3–19

  22. Ramchoun H, Ghanou Y, Ettaouil M, Janati Idrissi MA (2016) Multilayer perceptron: architecture optimization and training

  23. Saeed F, Paul A, Rho S (2020) Faster r-cnn based fault detection in industrial images. In: International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, Springer, pp 280–287

  24. Rathore MM, Paul A, Rho S, Khan M, Vimal S, Shah SA (2021) Smart traffic control: identifying driving-violations using fog devices with vehicular cameras in smart cities. Sustain Cities Soc 71:102986

    Article  Google Scholar 

  25. Nawaz H, Maqsood M, Afzal S, Aadil F, Mehmood I, Rho S (2021) A deep feature-based real-time system for alzheimer disease stage detection. Multimed Tools Appl 80(28):35789–35807

    Article  Google Scholar 

  26. Robinson YH, Vimal S, Julie EG, Lakshmi Narayanan K, Rho S (2021) 3-dimensional manifold and machine learning based localization algorithm for wireless sensor networks. Wireless Personal Communications, 1–19

  27. Fan S, Wang R, Wu Z, Rho S, Liu S, Xiong J, Fu S, Jiang F (2021) High-speed tracking based on multi-cf filters and attention mechanism. SIViP 15(4):663–671

    Article  Google Scholar 

  28. Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 580–587

  29. Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1440–1448

  30. Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 779–788

  31. Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7263–7271

  32. Liu S, Qi L, Qin H, Shi J, Jia J (2018) Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 8759–8768

  33. Zhang H, Cisse M, Dauphin YN, Lopez-Paz D (2017) mixup: Beyond empirical risk minimization. Preprint arXiv:1710.09412

  34. Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 6023–6032

  35. Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2020) Distance-iou loss: Faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 12993–13000

  36. Müller R, Kornblith S, Hinton GE (2019) When does label smoothing help? Advances in neural information processing systems, 32

  37. Tan Z, Wang J, Sun X, Lin M, Li H, et al (2021) Giraffedet: A heavy-neck paradigm for object detection. In: International Conference on Learning Representations

  38. Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp 764–773

  39. Paul S, Chen P-Y (2021) Vision transformers are robust learners. Preprint<error l="301" c="bad csname" />arXiv:2105.075812(3)

  40. Naseer MM, Ranasinghe K, Khan SH, Hayat M, Shahbaz Khan F, Yang M-H (2021) Intriguing properties of vision transformers. Advances in Neural Information Processing Systems, 34

  41. Srinivas A, Lin T-Y, Parmar N, Shlens J, Abbeel P, Vaswani A (2021) Bottleneck transformers for visual recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 16519–16529

  42. Chen Z, Xie L, Niu J, Liu X, Wei L, Tian Q (2021) Visformer: The vision-friendly transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 589–598

  43. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q Supplementary material for ‘eca-net: Efficient channel attention for deep convolutional neural networks. Technical report

  44. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser, Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems, 30

  45. Park N, Kim S (2022) How do vision transformers work? Preprint arXiv:2202.06709

  46. Misra D (2019) Mish: A self regularized non-monotonic activation function. Preprint arXiv:1908.08681

  47. Lin T-Y, Dollár P, Girshick R, He K, Hariharan B, Belongie S (2017) Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2117–2125

  48. He K, Zhang X, Ren S, Sun J (2015) Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans Pattern Anal Mach Intell 37(9):1904–1916

    Article  Google Scholar 

  49. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 248–255

  50. Krizhevsky A, Hinton G, et al (2009) Learning multiple layers of features from tiny images

  51. Le Y, Yang X (2015) Tiny imagenet visual recognition challenge. CS 231N 7(7):3

    Google Scholar 

  52. Hassani A, Walton S, Shah N, Abuduweili A, Li J, Shi H (2021) Escaping the big data paradigm with compact transformers. Preprint arXiv:2104.05704

  53. Zagoruyko S, Komodakis N (2016) Wide residual networks. Preprint arXiv:1605.07146

  54. Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context. In: European Conference on Computer Vision, Springer, pp 740–755

  55. RangiLyu (2021) NanoDet-Plus: Super fast and high accuracy lightweight anchor-free object detection model. https://github.com/RangiLyu/nanodet

Download references

Acknowledgements

The authors acknowledge XJTLU-JITRI Academy of Industrial Technology for giving the valuable support of the joint project. The authors also acknowledge Mr. Wenhan Tao for the support of GPU resources.

Author information

Author notes

  1. Runwei Guan and Ka Lok Man authors have contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, L69 3BX, UK

    Runwei Guan, Haocheng Zhao & Jeremy Smith

  2. School of Advanced Technology, Xi’an Jiaotong Liverpool University, Suzhou, 215123, China

    Runwei Guan, Ka Lok Man, Haocheng Zhao, Shanliang Yao & Eng Gee Lim

  3. Institute of Deep Perception Technology, JITRI, Wuxi, 214000, China

    Runwei Guan, Haocheng Zhao & Yutao Yue

  4. School of Electronics and Computer Science, University of Southampton, Southampton, SO17 1BJ, UK

    Ruixiao Zhang

Authors
  1. Runwei Guan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Ka Lok Man
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Haocheng Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ruixiao Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Shanliang Yao
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Jeremy Smith
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Eng Gee Lim
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Yutao Yue
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yutao Yue.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 506 KB)

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guan, R., Man, K.L., Zhao, H. et al. MAN and CAT: mix attention to nn and concatenate attention to YOLO. J Supercomput 79, 2108–2136 (2023). https://doi.org/10.1007/s11227-022-04726-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04726-7

Keywords