计算机与现代化 ›› 2023, Vol. 0 ›› Issue (02): 40-49.
出版日期:
2023-04-10
发布日期:
2023-04-10
作者简介:
皇甫晓瑛(1997—),女,河南安阳人,硕士研究生,研究方向:人体行为识别,机器视觉,深度学习,E-mail: 201306060013@hhu.edu.cn; 通信作者:钱惠敏(1980—),女,江苏宜兴人,副教授,博士,研究方向:计算机视觉,机器学习,E-mail: qhmin0316@163.com; 黄敏(1998—),女,江苏启东人,硕士研究生,研究方向:深度学习,视频中的人体行为识别,E-mail: 2458237010@qq.com。
基金资助:
Online:
2023-04-10
Published:
2023-04-10
摘要: 注意力机制已成为改进神经网络学习能力的研究热点之一。鉴于注意力机制受到的广泛关注,本文旨在从注意力机制的分类、与深度神经网络的结合方式,以及在自然语言处理和计算机视觉领域的具体应用3个方面对深度神经网络中的注意力机制给出较全面的分析和阐述。具体地,分析比较了软注意力、硬注意力和自注意力这3种机制的优缺点;并分别讨论了递归神经网络和卷积神经网络中结合注意力机制的常用方式及其代表性模型结构;然后,以自然语言处理、计算机视觉领域为例,说明了其应用情况;最后,分析了注意力机制的发展趋势,期望为后续研究提供线索和方向。
皇甫晓瑛, 钱惠敏, 黄敏. 结合注意力机制的深度神经网络综述[J]. 计算机与现代化, 2023, 0(02): 40-49.
HUANGFU Xiao-ying, QIAN Hui-min, HUANG Min. A Review of Deep Neural Networks Combined with Attention Mechanism[J]. Computer and Modernization, 2023, 0(02): 40-49.
[1] | 吴建鑫,高斌斌,魏秀参,等. 资源受限的深度学习:挑战与实践[J]. 中国科学:信息科学, 2018,48(5):501-510. |
[2] | BORJI A, ITTI L. State-of-the-art in visual attention modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013,35(1):185-207. |
[3] | 王培森. 基于注意力机制的图像分类深度学习方法研究[D]. 合肥:中国科学技术大学, 2018. |
[4] | HONG Z. A preliminary study on artificial neural network[C]// 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference. 2011:336-338. |
[5] | CHAUDHARI S, MITHAL V, POLATKAN G, et al. An Attentive Survey of Attention Models[J]. arXiv preprint arXiv:1904.02874, 2019. |
[6] | 任欢,王旭光. 注意力机制综述[J]. 计算机应用, 2021,41(S01):1-6. |
[7] | CORREIA A D S, COLOMBINI E L. Attention, please! A survey of neural attention models in deep learning[J]. arXiv preprint arXiv:2103.16775, 2021. |
[8] | XU K, BA J, KIROS R, et al. Show, attend and tell: Neural image caption generation with visual attention[J]. arXiv preprint arXiv:1502.03044v2, 2015. |
[9] | HU J, SHEN L, ALBANIE S, et al. Squeeze-and-xxcitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020,42(8):2011-2023. |
[10] | WANG Q L, WU B G, ZHU P F, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020:11531-11539. |
[11] | LI X, WANG W H, HU X L, et al. Selective kernel networks[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019:510-519. |
[12] | JADERBERG M, SIMONYAN K, ZISSERMAN A, et al. Spatial transformer networks[J]. arXiv preprint arXiv:1506.02025v3, 2015. |
[13] | WOO S, PARK J, LEE J, et al. CBAM: Convolutional block attention module[C]// Computer Vision - ECCV 2018. 2018. DOI:10.1007/978-3-030-01234-2_1. |
[14] | PARK J, WOO S, LEE J, et al. BAM: Bottleneck attention module[J]. arXiv preprint arXiv:1807.06514, 2018. |
[15] | ROY A G, NAVAB N, WACHINGER C. Concurrent spatial and channel squeeze & excitation in fully convolutional networks[J]. arXiv preprint arXiv:1803.02579v2, 2018. |
[16] | MNIH V, HEESS N, GRAVES A, et al. Recurrent models of visual attention[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2. 2014:2204-2212. |
[17] | MALINOWSKI M, DOERSCH C, SANTORO A, et al. Learning visual question answering by?Bootstrapping hard attention[C]// Computer Vision – ECCV 2018. 2018. DOI:10.1007/978-3-030-01231-1_1. |
[18] | ZHOU S K, LE H N, LU U K, et al. Deep reinforcement learning in medical imaging: A literature review[J]. arXiv preprint arXiv:2103.05115, 2021. |
[19] | MICHEL P, LEVY O, NEUBIG Graham. Are sixteen heads really better than one?[J]. arXiv preprint arXiv: 1905.10650, 2019. |
[20] | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. arXiv preprint arXiv:1706.03762v5, 2017. |
[21] | WANG X, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018:7794-7803. |
[22] | HUANG Z L, WANG X G, HUANG L C, et al. CCNet: Criss-cross attention for semantic segmentation[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). 2019:603-612. |
[23] | REN S C, ZHOU D Q, HE S F, et al. Shunted self-attention via multi-scale token aggregation[J]. arXiv preprint arXiv:2111.15193, 2021. |
[24] | PI H J, WANG H Y, LI Y W, et al. Searching for TrioNet: Combining convolution with local and global self-attention[J]. arXiv preprint arXiv:2111.07547, 2021. |
[25] | LIPTON Z C, BERKOWITZ J, ELKAN C. A critical review of recurrent neural networks for sequence learning[J]. arXiv preprint arXiv: 1506.00019v4, 2015. |
[26] | SEO M, KEMBHAVI A, FARHADI A, et al. Bidirectional attention flow for machine comprehension[J]. arXiv preprint arXiv:1611.01603v6, 2016. |
[27] | WANG B, LIU K, ZHAO J. Inner attention based recurrent neural networks for answer selection[C]// Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016:1288-1297. |
[28] | QIN Y, SONG D J, CHENG H F, et al. A dual-stage attention-based recurrent neural network for time series prediction[C]// Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017:2627-2633. |
[29] | ZHANG P F, XUE J R, LAN C L, et al. Adding attentiveness to the neurons in recurrent neural networks[C]// 15th European Conference on Munich, Germany. 2018:136-152. |
[30] | HUBEL D H, WIESEL T N. Early exploration of the visual cortex[J]. Neuron, 1998,20(3):401-412. |
[31] | XU S J, CHENG Y, GU K, et al. Jointly attentive spatial-temporal pooling networks for video-based person re-identification[C]// 2017 IEEE International Conference on Computer Vision (ICCV). 2017:4743-4752. |
[32] | LIU N, LONG Y C, ZOU C Q, et al. ADCrowdNet: An attention-injective deformable convolutional network for crowd understanding[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019:3220-3229. |
[33] | GUO S N, LIN Y F, FENG N, et al. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019,33(1):922-929. |
[34] | WANG F, JIANG M Q, QIAN C, et al. Residual attention network for image classification[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017:6450-6458 |
[35] | FU J L, ZHENG H L, MEI T. Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2017:4476-4484. |
[36] | YANG S H, WANG Y X, CHU X W. A survey of deep learning techniques for neural machine translation[J]. arXiv preprint arXiv:2002.07526v1, 2020. |
[37] | BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv:1409.0473v7, 2016. |
[38] | LUONG M, PHAM H, MANNING C D. Effective approaches to attention-based neural machine translation[J]. arXiv preprint arXiv:1508.04025, 2015. |
[39] | GEHRING J, AULI M, GRANGIER D, et al. Convolutional sequence to sequence learning[J]. arXiv preprint arXiv:1705.03122v3, 2017. |
[40] | DEVLIN J, CHANG M, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018. |
[41] | 杨小冈,高凡,卢瑞涛,等. 基于改进YOLOv5的轻量化航空目标检测方法[J]. 信息与控制, 2022,51(3):361-368. |
[42] | 刘赏,葛顶玉,耿明筱.结合全局与局部的人群集体性卷积网络识别方法 [J/OL].信息与控制.[2022-01-01] https://doi.org/10.13976/j.cnki.xk.2022.1381. |
[43] | CHEN T, LIU Y, SU H, et al. Dual-Awareness Attention for Few-Shot Object Detection[J]. arXiv preprint arXiv:2102.12152v3, 2021. |
[44] | NIU R, SUN X, TIAN Y, et al. Hybrid multiple attention network for semantic segmentation in aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing. 2021,60:1-18. |
[45] | FU J, LIU J, TIAN H J, et al. Dual attention network for scene segmentation[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019:3141-3149. |
[46] | WANG H, WANG W N, LIU J. Temporal memory attention for video semantic segmentation[J]. arXiv preprint arXiv:2102.08643, 2021. |
[47] | 于东飞. 基于注意力机制与高层语义的视觉问答研究[D]. 合肥:中国科学技术大学, 2019. |
[48] | GUO W Y, ZHANG Y, WU X F, et al. Re-attention for visual question answering[C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2020,34(1):91-98. |
[49] | 孟乐乐. 融合时空网络与注意力机制的人体行为识别研究[D]. 北京:北京交通大学, 2018. |
[50] | PEREZ-RUA J M, MARTINEZ B, ZHU X, et al. Knowing what, where and when to look: Efficient video action modeling with attention[J]. arXiv preprint arXiv: 2004.01278v1, 2020. |
[51] | PU S, SONG Y B, MA C, et al. Deep attentive tracking via reciprocative learning[J]. arXiv preprint arXiv:1810.03851, 2018. |
[52] | CAO Z, FU C H, YE J J, et al. SiamAPN++: Siamese attentional aggregation network for real-time UAV Tracking[J]. arXiv preprint arXiv:2106.08816v2, 2021. |
[53] | XUE Y, YUAN Z M, NERI F. ConAM: Confidence attention module for convolutional neural networks[J]. arXiv preprint arXiv:2110.14369, 2021. |
[54] | GUO M H, LU C Z, LIU Z N, et al. Visual attention network[J]. arXiv preprint arXiv:2202.09741, 2022. |
[55] | 姚懿秦,郭薇. 基于交互注意力机制的多模态情感识别算法[J]. 计算机应用研究, 2021,38(6):1689-1693. |
[56] | LIU H D, XU S Y, FU J M, et al. CMA-CLIP: Cross-modality attention CLIP for image-text classification[J]. arXiv preprint arXiv:2112.03562, 2021. |
[57] | HAFIZ A M, PARAH S A, BHAT R U A. Attention mechanisms and deep learning for machine vision: A survey of the state of the art[J]. arXiv preprint arXiv:2106.07550v1, 2021. |
[58] | 姚玉倩. 基于胶囊网络的人脸表情特征提取与识别算法研究[D]. 北京:北京交通大学, 2019. |
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