多階段注意力膠囊網(wǎng)絡(luò )的圖像分類(lèi)
doi: 10.16383/j.aas.c210012 cstr: 32138.14.j.aas.c210012
-
1.
上海理工大學(xué)控制科學(xué)與工程系 上海 200093
Multi-stage Attention-based Capsule Networks for Image Classification
-
1.
Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093
-
摘要: 針對傳統的膠囊網(wǎng)絡(luò )(Capsule network, CapsNet)特征提取不充分的問(wèn)題, 提出一種圖像分類(lèi)的多階段注意力膠囊網(wǎng)絡(luò )模型. 首先, 在卷積層對低層特征和高層特征分別采用注意力(Spatial attention, SA)和通道注意力(Channel attention, CA)來(lái)提取有效特征; 然后, 提出基于向量的注意力(Vector attention, VA)機制作用于動(dòng)態(tài)路由層, 增加對重要膠囊的關(guān)注, 進(jìn)而提高低層膠囊對高層膠囊預測的準確性; 最后, 在五個(gè)公共數據集上進(jìn)行圖像分類(lèi)的對比實(shí)驗. 結果表明, 所提出的CapsNet模型在分類(lèi)精度和魯棒性上優(yōu)于其他膠囊網(wǎng)絡(luò )模型, 在仿射變換圖像重構方面也表現良好.
-
關(guān)鍵詞:
- 圖像分類(lèi) /
- 膠囊網(wǎng)絡(luò ) /
- 注意力機制 /
- 多階段 /
- 魯棒性
Abstract: Aiming to address the inadequate feature extraction problems in the traditional capsule networks (CapsNets), a multi-stage attention-based CapsNet model is proposed in this paper for image classification. Firstly, spatial attention (SA) and channel attention (CA) are used to extract effective features in the convolutional layer from low-level features and high-level features, respectively. Then, attention mechanism based on vector direction is introduced into the dynamic routing layer to enhance the focus on the important capsules, thereby improving the prediction accuracy of the low-layer capsules to the high-layer capsules. Finally, the comparison experiments on image classification are carried out on five public datasets. The experimental results show that the proposed CapsNet outperforms other CapsNets at the classification accuracy and the robustness, and its shows a good performance on the image reconstruction for affine images.-
Key words:
- Image classification /
- capsule network (CapsNet) /
- attention mechanism /
- multi-stage /
- robustness
-
圖 6 不同改進(jìn)模塊在五個(gè)數據集上的迭代曲線(xiàn)
Fig. 6 Iteration curves of different improvement modules on five datasets
圖 9 比較MNIST數據集中的真實(shí)圖像、傳統膠囊網(wǎng)絡(luò )的重構圖像以及本文模型的重構圖像
Fig. 9 Comparison of the real images from the MNIST dataset, the reconstructions from a conventional capsule network, and the reconstructions from our model
圖 10 比較Fashion-MNIST 數據集中的真實(shí)圖像、傳統膠囊網(wǎng)絡(luò )的重構圖像以及本文模型的重構圖像
Fig. 10 Comparison of the real images from the Fashion-MNIST dataset, the reconstructions from a conventional capsule network, and the reconstructions from our model
圖 11 比較CIFAR-10 數據集中的真實(shí)圖像、傳統膠囊網(wǎng)絡(luò )的重構圖像以及本文模型的重構圖像
Fig. 11 Comparison of the real images from the CIFAR-10 dataset, the reconstructions from a conventional capsule network, and the reconstructions from our model
圖 12 比較SVHN 數據集中的真實(shí)圖像、傳統膠囊網(wǎng)絡(luò )的重構圖像以及本文模型的重構圖像
Fig. 12 Comparison of the real images from the SVHN dataset, the reconstructions from a conventional capsule network, and the reconstructions from our model
圖 13 比較smallNORB數據集中的真實(shí)圖像、傳統膠囊網(wǎng)絡(luò )的重構圖像以及本文模型的重構圖像
Fig. 13 Comparison of the real images from the smallNORB dataset, the reconstructions from a conventional capsule network, and the reconstructions from our model
圖 14 MINST數據集原圖和仿射變換圖
Fig. 14 Original image and affine transformations images of MINST dataset
圖 15 圖14(b)的重構實(shí)驗對比圖
Fig. 15 Comparison of reconstructions to Fig. 14(b)
圖 16 圖14(c)的重構實(shí)驗對比圖
Fig. 16 Comparison of reconstructions to Fig. 14(c)
表 1 不同改進(jìn)模塊在五個(gè)數據集上的分類(lèi)錯誤率(%)
Table 1 Classification error rates of different improvement modules on five datasets (%)
模型 MNIST Fashion-MNIST CIFAR-10 SVHN smallNORB Baseline 0.38 7.11 21.21 5.12 5.62 Baseline + (SA + CA) 0.32 5.54 11.69 4.61 5.07 Baseline + VA 0.28 5.53 14.65 4.99 5.21 Baseline + (SA + CA + VA) 0.22 4.63 9.99 4.08 4.89 
下載: 導出CSV
表 2 不同模型在五個(gè)數據集上的分類(lèi)錯誤率(%)
Table 2 Classification error rates of different models on five datasets (%)
模型 MNIST Fashion-MNIST CIFAR-10 SVHN smallNORB Prem Nair et al.＇s CapsNet[5] 0.50 10.20 31.47 8.94 — HitNet[7] 0.32 7.70 26.70 5.50 — Matrix Capsule EM-routing[9] 0.70 5.97 16.79 9.64 5.20 SACN[10] 0.50 5.98 16.65 5.01 7.79 AR-CapsNet[11] 0.54 — 12.71 — — DCNet[30] 0.25 5.36 17.37 4.42 5.57 MS-CapsNet[31] — 6.01 18.81 — — VB-routing[32] — 5.20 11.20 4.75 1.60 Aff-CapsNets[33] 0.46 7.47 23.72 7.85 — 本文模型 0.22 4.63 9.99 4.08 4.89
下載: 導出CSV
表 3 不同模型的魯棒性對比實(shí)驗(%)
Table 3 Robustness comparison test of different models (%)
下載: 導出CSV
亚洲第一网址_国产国产人精品视频69_久久久久精品视频_国产精品第九页<button id="qm3rj"><thead id="qm3rj"></thead></button> <samp id="qm3rj"></samp> <source id="qm3rj"><menu id="qm3rj"><pre id="qm3rj"></pre></menu></source> <video id="qm3rj"><code id="qm3rj"></code></video> <tt id="qm3rj"><track id="qm3rj"></track></tt> -
[1] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the Conference on Neural Information Processing Systems. Lake Tahoe, USA: NIPS, 2012. 1097−1105 [2] Simonyan K, Zissweman A. Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations. San Diego, USA: ICLR, 2015. 1?14 [3] Howard A G, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv: 1704.04861, 2017. [4] Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 2261?2269 [5] Nair P, Doshi R, Keselj S. Pushing the limits of capsule networks. arXiv preprint arXiv: 2103.08074, 2021. [6] Sabour S, Frosst N, Hinton G E. Dynamic routing between capsules. In: Proceedings of the Neural Information Processing Systems. Long Beach, USA: NIPS, 2017. 3856?3866 [7] Deliege A, Cioppa A, Van Droogenbroeck M. HitNet: A neural network with capsules embedded in a hit-or-miss layer, extended with hybrid data augmentation and ghost capsules. arXiv preprint arXiv: 1806.06519, 2018. [8] Xi E, Bing S, Jin Y. Capsule network performance on complex data. arXiv preprint arXiv: 1712.03480, 2017. [9] Hinton G E, Sabour S, Frosst N. Matrix capsules with EM routing. In: Proceedings of the International Conference on Learning Representations. Vancouver, Canada: ICLR, 2018. 1?15 [10] Hoogi A, Wilcox B, Gupta Y, Rubin D L. Self-attention capsule networks for object classification. arXiv preprint arXiv: 1904.12483, 2019. [11] Choi J, Seo H, Im S, Kang M. Attention routing between capsules. In: Proceedings of the IEEE International Conference on Computer Vision. Seoul, South Korea: IEEE, 2019. 1981?1989 [12] Wang X, Tu Z, Zhang M. Incorporating statistical machine translation word knowledge into neural machine translation. IEEE/ACM Transactions on Audio, Speech, and Language Proceeding, 2018, 26(12): 2255?2266 doi: 10.1109/TASLP.2018.2860287 [13] Zhang B, Xiong D, Su J. Neural machine translation with deep attention. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 42(1): 154?163 [14] Zhang B, Xiong D, Xie J, Su J. Neural machine translation with gru-gated attention model. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(11): 4688?4698 doi: 10.1109/TNNLS.2019.2957276 [15] 王金甲, 紀紹男, 崔琳, 夏靜, 楊倩. 基于注意力膠囊網(wǎng)絡(luò )的家庭活動(dòng)識別. 自動(dòng)化學(xué)報, 2019, 45(11): 2199?2204Wang Jin-Jia, Ji Shao-Nan, Cui Lin, Xia Jing, Yang Qian. Identification of family activities based on attention capsule network. Acta Automatica Sinica, 2019, 45(11): 2199?2204 [16] Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhutdinov R, et al. Show, attend and tell: Neural image caption generation with visual attention. In: Proceedings of the International Conference on Machine Learning. Lugano, Switzerland: ICML, 2015. 2048?2057 [17] Gao L, Li X, Song J, Shen H T. Hierarchical lstms with adaptive attention for visual captioning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 42(5): 1112?1131 [18] Lu X, Wang B, Zheng X. Sound active attention framework for remote sensing image captioning. IEEE Transactions on Geoscience and Remote Sensing, 2019, 58(3): 1985?2000 [19] Wang X, Duan H. Hierarchical visual attention model for saliency detection inspired by avian pathways. IEEE/CAA Journal of Automatica Sinica, 2017, 6(2): 540?552 [20] Xu H, Saenko K. Ask, attend and answer: Exploring question-guided spatial attention for visual question answering. In: Proceedings of the European Conference on Computer Vision. Amsterdam, The Netherlands: ECCV, 2016. 451?466 [21] Liang J, Jiang L, Cao L, Kalantidis Y, Li L J, Hauptmann A G. Focal visual-text attention for memex question answering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(8): 1893?1908 doi: 10.1109/TPAMI.2018.2890628 [22] 肖進(jìn)勝, 申夢(mèng)瑤, 江明俊, 雷俊峰, 包振宇. 融合包注意力機制的監控視頻異常行為檢測. 自動(dòng)化學(xué)報, 2022, 48(12): 2951?2959Xiao Jin-Sheng, Shen Meng-Yao, Jiang Ming-Jun, Lei Jun-Feng, Bao Zhen-Yu. Abnormal behavior detection algorithm with video-bag attention mechanism in surveillance video. Acta Automatica Sinica, 2022, 48(12): 2951?2959 [23] Zhao X, Chen Y, Guo J, Zhao D. A spatial-temporal attention model for human trajectory prediction. IEEE/CAA Journal of Automatica Sinica, 2020, 7(4): 965?974 doi: 10.1109/JAS.2020.1003228 [24] 王亞珅, 黃河燕, 馮沖, 周強. 基于注意力機制的概念化句嵌入研究. 自動(dòng)化學(xué)報, 2020, 46(7): 1390?1400Wang Ya-Kun, Huang He-Yan, Feng Chong, Zhou Qiang. A study of conceptual sentence embedding based on attentional mechanism. Acta Automatica Sinica, 2020, 46(7): 1390?1400 [25] 馮建周, 馬祥聰. 基于遷移學(xué)習的細粒度實(shí)體分類(lèi)方法的研究. 自動(dòng)化學(xué)報, 2020, 46(8): 1759?1766Feng Jian-Zhou, Ma Xiang-Cong. Research on fine-grained entity classification method based on transfer learning. Acta Automatica Sinica, 2020, 46(8): 1759?1766 [26] 王縣縣, 禹龍, 田生偉, 王瑞錦. 獨立RNN和膠囊網(wǎng)絡(luò )的維吾爾語(yǔ)事件缺失元素填充. 自動(dòng)化學(xué)報, 2021, 47(4): 903?912Wang Xian-Xian, Yu Long, Tian Sheng-Wei, Wang Rui-Jin. Independent RNN and CAPE networks were populated with missing elements of Uyghur events. Acta Automatica Sinica, 2021, 47(4): 903?912 [27] Wang X, Girshick R, Gupta A, He K. Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake, USA: IEEE, 2018. 7794?7803 [28] Woo S, Park J, Lee J Y, Kweon I S. Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision. Munich, Germany: ECCV, 2018. 3?19 [29] Hu J, Shen L, Sun G, Wu E. Squeeze-and-excitation networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011?2023 doi: 10.1109/TPAMI.2019.2913372 [30] Phaye S S R, Sikka A, Dhall A, Bathula D. Dense and diverse capsule networks: Making the capsules learn better. arXiv preprint arXiv: 1805.04001, 2018. [31] Xiang C, Zhang L, Tang Y, Zou W, Xu C. MS-CapsNet: A novel multi-scale capsule network. IEEE Signal Processing Letters, 2018, 25(12): 1850?1854 doi: 10.1109/LSP.2018.2873892 [32] Ribeiro F D S, Leontidis G, Kollias S. Capsule routing via variational bayes. In: Proceedings of the AAAI Conference on Artificial Intelligence. New York, USA: AAAI, 2020. 3749?3756 [33] Gu J, Tresp V. Improving the robustness of capsule networks to image affine transformation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE, 2020. 7283?7291 -
計量
- 文章訪(fǎng)問(wèn)數: 1755
- HTML全文瀏覽量: 1169
- PDF下載量: 216
- 被引次數: 0
