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SIS: A new multi-scale convolutional operator

  • School of Information Science and Tecnology, University of Science and Technology of China, Hefei 230027 China

Funds:  This work was supported by the USTC Research Funds of the Double First-Class Initiative under Grants YD2100002003 and YD2100002004.
Cite this:
https://doi.org/10.52396/JUSTC-2021-0188
More Information
  • Author Bio:

    Man Zhou is currently working toward the PhD degree in University of Science and Technology of China, Hefei, China. His research interests include image/video processing, computer vision

    Xueyang Fu received the PhD degree in signal and information processing from Xiamen University, Xiamen, China, in 2018. He was a Visiting Scholar with Columbia University, New York, USA, sponsored by the China Scholarship Council, from 2016 to 2017. He is currently an Associate Researcher with the Department of Automation, University of Science and Technology of China, Hefei, China. His research interests include machine learning and image processing

  • Corresponding author: E-mail: xyfu@ustc.edu.cn
  • Received Date: 27 August 2021
  • Accepted Date: 09 February 2022
    Abstract

  • Abstract

    Visual features with high potential for generalization are critical for computer vision applications. In addition to the computational overhead associated with layer-by-layer feature stacking to produce multi-scale feature maps, existing approaches also incur high computational costs. To address this issue, we present a compact and efficient scale-in-scale convolution operator called SIS by incorporating an efficient progressive multi-scale architecture into a standard convolution operator. More precisely, the suggested operator uses the channel transform-divide-and-conquer technique to optimize conventional channel-wise computing, thereby lowering the computational cost while simultaneously expanding the receptive fields within a single convolution layer. Moreover, the proposed SIS operator incorporates weight-sharing with split-and-interact and recur-and-fuse mechanisms for enhanced variant design. The suggested SIS series is easily pluggable into any promising convolutional backbone, such as the well-known ResNet and Res2Net. Furthermore, we incorporated the proposed SIS operator series into 29-layer, 50-layer, and 101-layer ResNet as well as Res2Net variants and evaluated these modified models on the widely used CIFAR, PASCAL VOC, and COCO2017 benchmark datasets, where they consistently outperformed state-of-the-art models on a variety of major vision tasks, including image classification, key point estimation, semantic segmentation, and object detection.

    Graphical abstract

      We design a series of transformation mechanism to expand the module (a) and Res2Net module (b) into multi-scale module operators (c, d, e, f, g).

    Abstract

    Visual features with high potential for generalization are critical for computer vision applications. In addition to the computational overhead associated with layer-by-layer feature stacking to produce multi-scale feature maps, existing approaches also incur high computational costs. To address this issue, we present a compact and efficient scale-in-scale convolution operator called SIS by incorporating an efficient progressive multi-scale architecture into a standard convolution operator. More precisely, the suggested operator uses the channel transform-divide-and-conquer technique to optimize conventional channel-wise computing, thereby lowering the computational cost while simultaneously expanding the receptive fields within a single convolution layer. Moreover, the proposed SIS operator incorporates weight-sharing with split-and-interact and recur-and-fuse mechanisms for enhanced variant design. The suggested SIS series is easily pluggable into any promising convolutional backbone, such as the well-known ResNet and Res2Net. Furthermore, we incorporated the proposed SIS operator series into 29-layer, 50-layer, and 101-layer ResNet as well as Res2Net variants and evaluated these modified models on the widely used CIFAR, PASCAL VOC, and COCO2017 benchmark datasets, where they consistently outperformed state-of-the-art models on a variety of major vision tasks, including image classification, key point estimation, semantic segmentation, and object detection.

    • Public Summary

    • A more lightweight and representative scale-in-scale operator, namely SIS is proposed. The operator can be plugged into any promising backbone to replace regular convolution operator.
    • To be more efficient, an improved SIS series is proposed. The series maintains promising results within nearly half parameters.
    • Extensive experiments demonstrate the superior performance of our SIS operator series compared with state-of-the-art methods on image classification, object detection, key points estimation and semantic segmentation.

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    • References(29)
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    Figure  1.  Comparison between bottleneck modules(a), Res2Net module(b) and SIS module series(c, d, e, f, g) and illustration of scale-in-scale mechanism (h).

    Figure  2.  Visual comparisons between Faster RCNN and SIS equipped Faster RCNN.

    [1]
    Wang Q, Chen W, Wu X, et al. Detail preserving multi-scale exposure fusion. In: 2018 25th IEEE International Conference on Image Processing (ICIP). Athens, Greece: IEEE, 2018: 1713-1717.
    [2]
    Wang B, Lei Y, Li N, et al. Multi-scale convolutional attention network for predicting remaining useful life of machinery. IEEE Transactions on Industrial Electronics, 2021, 68 (8): 7496–7504. doi: 10.1109/TIE.2020.3003649
    [3]
    Yu J, Xie H, Xie G, et al. Multi-scale densely U-Nets refine network for face alignment. In: 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). Shanghai, China: IEEE, 2019: 691–694.
    [4]
    Zhang X, Zhang W. Application of new multi-scale edge fusion algorithm in structural edge extraction of aluminum foam. IEEE Access, 2020, 8: 15502–15517. doi: 10.1109/ACCESS.2019.2963454
    [5]
    Liu G, Wang C, Hu Y. RPN with the attention-based multi-scale method and the adaptive non-maximum suppression for billboard detection. In: 4th International Conference on Computer and Communications. Chengdu, China: IEEE, 2018: 2018.8780907.
    [6]
    Sun K, Xiao B, Liu D, et al. Deep high-resolution representation learning for human pose estimation. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA: IEEE, 2019: 5686–5696.
    [7]
    Fang X, Yan P. Multi-organ segmentation over partially labeled datasets with multi-scale feature abstraction. IEEE Transactions on Medical Imaging, 2020, 39 (11): 3619–3629. doi: 10.1109/TMI.2020.3001036
    [8]
    Huang G, Chen D, Li T, et al. Multi-scale dense networks for resource efficient image classification. In: 6th International Conference on Learning Representations. Vancouver, Canada: ICLR, 2018.
    [9]
    Moukari M, Picard S, Simon L, et al. Deep multi-scale architectures for monocular depth estimation. In: 2018 25th IEEE International Conference on Image Processing. Athens, Greece: IEEE, 2018: 2940–2944.
    [10]
    Papyan V, Elad M. Multi-scale patch-based image restoration. IEEE Transactions on Image Processing, 2016, 25 (1): 249–261. doi: 10.1109/TIP.2015.2499698
    [11]
    Li J, Fang F, Li J, et al. MDCN: Multi-scale dense cross network for image super-resolution. IEEE Transactions on Circuits and Systems for Video Technology, 2021, 31 (7): 2547–2561. doi: 10.1109/TCSVT.2020.3027732
    [12]
    Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60 (2): 91–110. doi: 10.1023/B:VISI.0000029664.99615.94
    [13]
    Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Boston, MA: IEEE, 2015: 1–9.
    [14]
    Gao S, Cheng M. M, Zhao K, et al. Res2Net: A new multi-scale backbone architecture. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 43 (2): 652–662. doi: 10.1109/TPAMI.2019.2938758
    [15]
    Krizhevsky A, Sutskever I, Hinton G. E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems: Volume 1. Red Hook, NY: Curran Associates Inc, 2012: 1097–1105.
    [16]
    Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. https://arxiv.org/abs/1409.1556.
    [17]
    He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV: IEEE, 2016: 770-778.
    [18]
    Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI: IEEE, 2017: 5987–5995.
    [19]
    Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI: IEEE, 2017: 2261–2269.
    [20]
    Iandola F N, Han S, Moskewicz M W, et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. In: 5th International Conference on Learning Representations. Toulon, France: ICLR, 2017.
    [21]
    Howard A G, Zhu M, Chen B, et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications. https://arxiv.org/abs/1704.04861.
    [22]
    Zhang X, Zhou X, Lin M, et al. ShuffleNet: An extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018: 6848–6856.
    [23]
    Lin M, Chen Q, Yan S. Network in network. In: ICLR 2014 Conference.Banff, Canada: ICLR, 2014.
    [24]
    Sun S, Pang J, Shi J, et al. FishNet: A versatile backbone for image, region, and pixel level prediction. Advances in Neural Information Processing Systems 31 (NeurIPS 2018). Montréal, Canada: NeurIPS, 2018: 754–764.
    [25]
    Yu F, Wang D, Shelhamer E, et al. Deep layer aggregation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT: IEEE, 2018: 2403–2412.
    [26]
    Liao R, Zhao Z, Urtasun R, et al. LanczosNet: Multi-scale deep graph convolutional networks. In: Seventh International Conference on Learning Representations. New Orleans, LA: ICLR, 2019.
    [27]
    Liu Y, Wang Y, Wang S, et al. CBNet: A novel composite backbone network architecture for object detection. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34 (7): 11653–11660. doi: 10.1609/aaai.v34i07.6834
    [28]
    Chen L, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Computer Vision–ECCV 2018. Cham, Switzerland: Springer, 2018: 833-851.
    [29]
    Xiao B, Wu H, Wei Y. Simple baselines for human pose estimation and tracking. In: Computer Vision–ECCV 2018. Cham, Switzerland: Springer, 2018: 472–487
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