计算机工程 ›› 2020, Vol. 46 ›› Issue (10): 1-17.doi: 10.19678/j.issn.1000-3428.0058018
景庄伟1a, 管海燕1b, 彭代峰1b, 于永涛2
收稿日期:
2020-04-09
修回日期:
2020-05-26
发布日期:
2020-06-02
作者简介:
景庄伟(1996-),男,硕士研究生,主研方向为计算机视觉;管海燕(通信作者),教授、博士;彭代峰,博士;于永涛,副教授、博士。
基金项目:
JING Zhuangwei1a, GUAN Haiyan1b, PENG Daifeng1b, YU Yongtao2
Received:
2020-04-09
Revised:
2020-05-26
Published:
2020-06-02
摘要: 随着深度学习技术的快速发展及其在语义分割领域的广泛应用,语义分割效果得到显著提升。对基于深度神经网络的图像语义分割方法进行分析与总结,根据网络训练方式的不同,将现有的图像语义分割分为全监督学习图像语义分割和弱监督学习图像语义分割,对每种方法中代表性算法的效果以及优缺点进行对比与分析,并阐述深度神经网络对语义分割领域的贡献。在此基础上,归纳当前主流的公共数据集和遥感数据集,对比主要的图像语义分割方法的分割性能,探讨当前语义分割技术面临的挑战并对其未来的发展方向进行展望。
中图分类号:
景庄伟, 管海燕, 彭代峰, 于永涛. 基于深度神经网络的图像语义分割研究综述[J]. 计算机工程, 2020, 46(10): 1-17.
JING Zhuangwei, GUAN Haiyan, PENG Daifeng, YU Yongtao. Survey of Research in Image Semantic Segmentation Based on Deep Neural Network[J]. Computer Engineering, 2020, 46(10): 1-17.
[1] YU H,YANG Z,TAN L.Methods and datasets on semantic segmentation:a review[J].Neurocomputing,2018,304:82-103. [2] COATES A,NG A Y.Learning feature representations with K-means[M]//XIAO G,SHAN W,SYSTEMS O,et al.Lecture notes in computer science.Berlin,Germany:Springer,2012:561-580. [3] WANG H Y,PAN D L,XIA D S.A fast algorithm for two-dimensional OTSU adaptive threshold algorithm[J].Acta Automatica Sinica,2005,33(9):969-970. [4] SHOTTON J,JOHNSON M,CIPOLLA R.Semantic texton forests for image categorization and segmentation[C]//Proceedings of 2008 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2008:1-8. [5] SU Jinling,WANG Zhaohui.An image segmentation method based on Graph Cut and super pixels in nature scene[J].Journal of Soochow University (Natural Science Edition),2012(2):27-33.(in Chinese)苏金玲,王朝晖.基于Graph Cut和超像素的自然场景显著对象分割方法[J].苏州大学学报(自然科学版),2012(2):27-33. [6] LATEEF F,RUICHEK Y.Survey on semantic segmentation using deep learning techniques[J].Neurocomputing,2019,338:321-348. [7] LIANG Xinyu,LUO Chen,QUAN Jichuan,et al.Research on progress of image semantic segmentation based on deep learning[J].Computer Engineering and Applications,2020,56(2):18-28.(in Chinese)梁新宇,罗晨,权冀川,等.基于深度学习的图像语义分割技术研究进展[J].计算机工程与应用,2020,56(2):18-28. [8] MINAEE S,BOYKOV Y,PORIKLI F,et al.Image segmentation using deep learning:a survey[EB/OL].[2020-03-25].https://arxiv.org/pdf/2001.05566.pdf. [9] TIAN Xuan,WANG Liang,DING Qi.Review of image semantic segmentation based on deep learning[J].Journal of Software,2019,30(2):440-468.(in Chinese)田萱,王亮,丁琪.基于深度学习的图像语义分割方法综述[J].软件学报,2019,30(2):440-468. [10] LONG J,SHELHAMER E,DARRELL T.Fully convolutional networks for semantic segmentation[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2014,39(4):640-651. [11] CHEN L C,PAPANDREOU G,KOKKINOS I,et al.Semantic image segmentation with deep convolutional nets and fully connected CRFS[J].International Conference on Learning Representations,2014(4):357-361. [12] CHEN L C,PAPANDREOU G,KOKKINOS I,et al.DeepLab:semantic image segmentation with deep convolutional nets,atrous convolution,and fully connected CRFs[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(4):834-848. [13] CHEN L C,PAPANDREOU G,SCHROFF F,et al.Rethinking atrous convolution for semantic image segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:252-263. [14] WANG P Q,CHEN P F,YUAN Y,et al.Understanding convolution for semantic segmentation[EB/OL].[2020-03-25].https://arxiv.org/abs/1702.08502. [15] ZHAO H S,SHI J P,QI X J,et al.Pyramid scene parsing network[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:2881-2890. [16] CHEN L C,ZHU Y,PAPANDREOU G,et al.Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of European Conference on Computer Vision.Berlin,Germany:Springer,2018:801-818. [17] RONNEBERGER O,FISCHER P,BROX T.U-Net:convolu-tional networks for biomedical image segmentation[C]//Proceedings of International Conference on Medical Image Computing and Computer-Assisted Intervention.Berlin,Germany:Springer,2015:234-241. [18] ZHOU Z W,RAHMAN SIDDIQUEE M M,TAJBAKHSH N,et al.UNet++:a nested U-net architecture for medical image segmentation[M]//CARDOSO M J,ARBEL T,CARNEIRO G,et al.Deep learning in medical image analysis and multimodal learning for clinical decision support.Berlin,Germany:Springer,2018:3-11. [19] BADRINARAYANAN V,KENDALL A,CIPOLLA R.SegNet:a deep convolutional encoder-decoder architecture for image segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(12):2481-2495. [20] KENDALL A,BADRINARAYANAN V,CIPOLLA R.Bayesian SegNet:model uncertainty in deep convolutional encoder-decoder architectures for scene understanding[C]//Proceedings of British Machine Vision Conference.London,UK:British Machine Vision Association,2017:1-12. [21] NOH H,HONG S,HAN B.Learning deconvolution network for semantic segmentation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:1520-1528. [22] PASZKE A,CHAURASIA A,KIM S,et al.ENet:a deep neural network architecture for real-time semantic segmentation[EB/OL].[2020-03-25].https://arxiv.org/abs/1606.02147. [23] LI H C,XIONG P F,FAN H Q,et al.DFANet:deep feature aggregation for real-time semantic segmentation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:9522-9531. [24] WANG Y,ZHOU Q,LIU J,et al.LEDNet:a lightweight encoder-decoder network for real-time semantic segmentation[C]//Proceedings of 2019 IEEE International Conference on Image Processing.Washington D.C.,USA:IEEE Press,2019:1860-1864. [25] TIAN Z,HE T,SHEN C H,et al.Decoders matter for semantic segmentation:data-dependent decoding enables flexible feature aggregation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:3126-3135. [26] PENG C,ZHANG X Y,YU G,et al.Large kernel matters-improve semantic segmentation by global convolutional network[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:4353-4361. [27] FU J,LIU J,WANG Y H,et al.Stacked deconvolutional network for semantic segmentation[EB/OL].[2020-03-25].https://arxiv.org/pdf/1708.04943.pdf. [28] ZHAO H S,ZHANG Y,LIU S,et al.PSANet:point-wise spatial attention network for scene parsing[M].Berlin,Germany:Springer,2018:270-286. [29] HUANG Z L,WANG X G,HUANG L C,et al.CCNet:criss-cross attention for semantic segmentation[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2019:603-612. [30] YU C Q,WANG J B,PENG C,et al.BiSeNet:bilateral segmentation network for real-time semantic segmenta-tion[M].Berlin,Germany:Springer,2018:334-349. [31] HU X X,YANG K L,FEI L,et al.ACNet:attention based network to exploit complementary features for RGBD semantic segmentation[C]//Proceedings of 2019 IEEE International Conference on Image Processing.Washington D.C.,USA:IEEE Press,2019:1440-1444. [32] NIU R G,SUN X,DIAO W H,et al.HMANet:hybrid multiple attention network for semantic segmentation in aerial images[EB/OL].[2020-03-25].https://arxiv.org/abs/2001.02870. [33] MNIH V,HEESS N,GRAVES A.Recurrent models of visual attention[M].Cambridge,USA:MIT Press,2014. [34] WANG X L,GIRSHICK R,GUPTA A,et al.Non-local neural networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:7794-7803. [35] FU J,LIU J,TIAN H J,et al.Dual attention network for scene segmentation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:3146-3154. [36] LI X,ZHONG Z S,WU J L,et al.Expectation-maximization attention networks for semantic segmentation[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2019:9167-9176. [37] LIN G,SHEN C,REID I,et al.Deeply learning the messages in message passing inference[M].Cambridge,USA:MIT Press,2015. [38] ARNAB A,JAYASUMANA S,ZHENG S,et al.Higher order conditional random fields in deep neural networks[M].Berlin,Germany:Springer,2016. [39] VEMULAPALLI R,TUZEL O,LIU M Y,et al.Gaussian conditional random field network for semantic segmentation[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:3224-3233. [40] SHEN F L,GAN R,YAN S C,et al.Semantic segmentation via structured patch prediction,context CRF and guidance CRF[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:1953-1961. [41] JIANG J D,ZHANG Z J,HUANG Y Q,et al.Incorporating depth into both CNN and CRF for indoor semantic segmentation[C]//Proceedings of 2017 IEEE International Conference on Software Engineering and Service Science.Washington D.C.,USA:IEEE Press,2017:525-530. [42] LIU Z W,LI X X,LUO P,et al.Semantic image segmentation via deep parsing network[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:1377-1385. [43] LIU Y F,CHEN K,LIU C,et al.Structured knowledge distillation for semantic segmentation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:2604-2613. [44] LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature pyramid networks for object detection[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:2117-2125. [45] YANG M K,YU K,ZHANG C,et al.DenseASPP for semantic segmentation in street scenes[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:3684-3692. [46] HE J J,DENG Z Y,QIAO Y.Dynamic multi-scale filters for semantic segmentation[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2019:3562-3572. [47] ZHAO H S,QI X J,SHEN X Y,et al.ICNet for real-time semantic segmentation on high-resolution images[M].Berlin,Germany:Springer,2018. [48] HE J J,DENG Z Y,ZHOU L,et al.Adaptive pyramid context network for semantic segmentation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:7519-7528. [49] WU H K,ZHANG J G,HUANG K Q,et al.FastFCN:rethinking dilated convolution in the backbone for semantic segmentation[EB/OL].[2020-03-25].https://arxiv.org/abs/1903.11816. [50] LUC P,COUPRIE C,CHINTALA S,et al.Semantic segmentation using adversarial networks[EB/OL].[2020-03-25].https://arxiv.org/abs/1611.08408. [51] HOFFMAN J,WANG D,YU F,et al.FCNs in the wild:pixel-level adversarial and constraint-based adaptation[EB/OL].[2020-03-25].https://arxiv.org/pdf/1612.02649.pdf. [52] XUE Y,XU T,ZHANG H,et al.Segan:adversarial network with multi-scale loss for medical image segmentation[J].Neuroinformatics,2018,16(3/4):383-392. [53] MAJURSKI M,MANESCU P,PADI S,et al.Cell image segmentation using generative adversarial networks,transfer learning,and augmentations[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:15-26. [54] TANG Y B,CAI J Z,LU L,et al.CT image enhancement using stacked generative adversarial networks and transfer learning for lesion segmentation improvement[M]//WERNICK M,YANG Y,BRANKOV J,et al.Machine learning in medical imaging.Berlin,Germany:Springer,2018:46-54. [55] HUNG W,TSAI Y H,LIOU Y T,et al.Adversarial learning for semi-supervised semantic segmentation[EB/OL].[2020-03-25].https://arxiv.org/abs/1802.07934. [56] YU F,KOLTUN V.Multi-scale context aggregation by dilated convolutions[EB/OL].[2020-03-25].https://arxiv.org/abs/1511.07122. [57] WANG P Q,CHEN P F,YUAN Y,et al.Understanding convolution for semantic segmentation[C]//Proceedings of 2018 IEEE Winter Conference on Applications of Computer Vision.Washington D.C.,USA:IEEE Press,2018:1451-1460. [58] MEHTA S,RASTEGARI M,CASPI A,et al.ESPNet:efficient spatial pyramid of dilated convolutions for semantic segmentation[M].Berlin,Germany:Springer,2018:561-580. [59] HU H,ZHANG Z,XIE Z D,et al.Local relation networks for image recognition[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2019:3464-3473. [60] TAKIKAWA T,ACUNA D,JAMPANI V,et al.Gated-SCNN:gated shape CNNs for semantic segmentation[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2019:5229-5238. [61] VISIN F,ROMERO A,CHO K,et al.ReSeg:a recurrent neural network-based model for semantic segmentation[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops.Washington D.C.,USA:IEEE Press,2016:41-48. [62] LI Z,GAN Y K,LIANG X D,et al.LSTM-CF:unifying context modeling and fusion with LSTMs for RGB-D scene labeling[M].Berlin,Germany:Springer,2016:541-557. [63] LIANG X D,SHEN X H,FENG J S,et al.Semantic object parsing with graph LSTM[M].Berlin,Germany:Springer,2016:125-143. [64] LIANG X D,LIN L,SHEN X H,et al.Interpretable structure-evolving LSTM[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:1010-1019. [65] ZHENG S,JAYASUMANA S,ROMERA-PAREDES B,et al.Conditional random fields as recurrent neural networks[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:1529-1537. [66] DAI J F,HE K M,SUN J.BoxSup:exploiting bounding boxes to supervise convolutional networks for semantic segmentation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:1635-1643. [67] RAJCHL M,LEE M C H,OKTAY O,et al.DeepCut:object segmentation from bounding box annotations using convolutional neural networks[J].IEEE Transactions on Medical Imaging,2017,36(2):674-683. [68] SONG C F,HUANG Y,OUYANG W L,et al.Box-driven class-wise region masking and filling rate guided loss for weakly supervised semantic segmentation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:3136-3145. [69] LIN D,DAI J F,JIA J Y,et al.ScribbleSup:scribble-supervised convolutional networks for semantic segmentation[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:3159-3167. [70] TANG M,DJELOUAH A,PERAZZI F,et al.Normalized cut loss for weakly-supervised CNN segmentation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:1818-1827. [71] OBUKHOV A,GEORGOULIS S,DAI D X,et al.Gated CRF loss for weakly supervised semantic image segmentation[EB/OL].[2020-03-25].https://arxiv.org/abs/1906.04651. [72] BEARMAN A,RUSSAKOVSKY O,FERRARI V,et al.What's the point:semantic segmentation with point supervision[M].Berlin,Germany:Springer,2016:549-565. [73] MANINIS K K,CAELLES S,PONT-TUSET J,et al.Deep extreme cut:from extreme points to object segmentation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:616-625. [74] PINHEIRO P O,COLLOBERT R.From image-level to pixel-level labeling with convolutional networks[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:1713-1721. [75] PAPANDREOU G,CHEN L C,MURPHY K P,et al.Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation[C]//Proceedings of 2015 IEEE International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2015:16-23. [76] DURAND T,MORDAN T,THOME N,et al.WILDCAT:weakly supervised learning of deep ConvNets for image classification,pointwise localization and segmentation[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:642-651. [77] KOLESNIKOV A,LAMPERT C H.Seed,expand and constrain:three principles for weakly-supervised image segmentation[M].Berlin,Germany:Springer,2016:695-711. [78] HUANG Z L,WANG X G,WANG J S,et al.Weakly-supervised semantic segmentation network with deep seeded region growing[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:7014-7023. [79] WEI Y C,XIAO H X,SHI H H,et al.Revisiting dilated convolution:a simple approach for weakly-and semi-supervised semantic segmentation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:7268-7277. [80] AHN J,KWAK S.Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:4981-4990. [81] ZHOU Y Z,ZHU Y,YE Q X,et al.Weakly supervised instance segmentation using class peak response[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2018:3791-3800. [82] WEI Y C,LIANG X D,CHEN Y P,et al.STC:a simple to complex framework for weakly-supervised semantic segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(11):2314-2320. [83] XU J,SCHWING A G,URTASUN R.Learning to segment under various forms of weak supervision[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:3781-3790. [84] HONG S,NOH H,HAN B.Decoupled deep neural network for semi-supervised semantic segmentation[M].Cambridge,USA:MIT Press,2015:1495-1503. [85] IBRAHIM M S,VAHDAT A,RANJBAR M,et al.Semi-supervised semantic image segmentation with self-correcting networks[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2020:159-168. [86] ZHENG Baoyu,WANG Yu,WU Jinwen,et al.Weakly supervised learning based on deep convolutional neural networks for image semantic segmentation[J].Journal of Nanjing University of Posts and Telecommunications(Natural Science Edition),2018,38(5):1-12.(in Chinese)郑宝玉,王雨,吴锦雯,等.基于深度卷积神经网络的弱监督图像语义分割[J].南京邮电大学学报(自然科学版),2018,38(5):1-12. [87] HONG S,YEO D,KWAK S,et al.Weakly supervised semantic segmentation using Web-crawled videos[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:7322-7330. [88] HONG S,OH J,LEE H,et al.Learning transferrable knowledge for semantic segmentation with deep convolutional neural network[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:3204-3212. [89] KALLURI T,VARMA G,CHANDRAKER M,et al.Universal semi-supervised semantic segmentation[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision.Washington D.C.,USA:IEEE Press,2019:5259-5270. [90] BROSTOW G J,FAUQUEUR J,CIPOLLA R.Semantic object classes in video:a high-definition ground truth database[J].Pattern Recognition Letters,2009,30(2):88-97. [91] LIU C,YUEN J,TORRALBA A.Nonparametric scene parsing:label transfer via dense scene alignment[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2009:20-36. [92] EVERINGHAM M,ESLAMI S M A,VAN GOOL L,et al.The pascal visual object classes challenge:a retrospective[J].International Journal of Computer Vision,2015,111(1):98-136. [93] SILBERMAN N,HOIEM D,KOHLI P,et al.Indoor segmentation and support inference from RGBD images[M].Berlin,Germany:Springer,2012:746-760. [94] PREST A,LEISTNER C,CIVERA J,et al.Learning object class detectors from weakly annotated video[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2012:3282-3289. [95] MOTTAGHI R,CHEN X J,LIU X B,et al.The role of context for object detection and semantic segmentation in the wild[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:891-898. [96] GEIGER A,LENZ P,STILLER C,et al.Vision meets robotics:the KITTI dataset[J].The International Journal of Robotics Research,2013,32(11):1231-1237. [97] CHEN X J,MOTTAGHI R,LIU X B,et al.Detect what you can:detecting and representing objects using holistic models and body parts[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2014:1971-1978. [98] BELL S,UPCHURCH P,SNAVELY N,et al.Material recognition in the wild with the materials in context database[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:3479-3487. [99] LIN T Y,MAIRE M,BELONGIE S,et al.Microsoft COCO:common objects in context[M].Berlin,Germany:Springer,2014:740-755. [100] SONG S R,LICHTENBERG S P,XIAO J X.SUN RGB-D:a RGB-D scene understanding benchmark suite[C]//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2015:567-576. [101] PERAZZI F,PONT-TUSET J,MCWILLIAMS B,et al.A benchmark dataset and evaluation methodology for video object segmentation[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:724-732. [102] CORDTS M,OMRAN M,RAMOS S,et al.The CityScapes Dataset for semantic urban scene understanding[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:3213-3223. [103] ROS G,SELLART L,MATERZYNSKA J,et al.The SYNTHIA dataset:a large collection of synthetic images for semantic segmentation of urban scenes[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:3234-3243. [104] ZHOU B L,ZHAO H,PUIG X,et al.Scene parsing through ADE20K dataset[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2017:633-641. [105] WANG P,HUANG X Y,CHENG X J,et al.The ApolloScape open dataset for autonomous driving and its application[EB/OL].[2020-03-25].https://arxiv.org/pdf/1803.06184v3.pdf. [106] ZOU Q,NI L H,ZHANG T,et al.Deep learning based feature selection for remote sensing scene classification[J].IEEE Geoscience and Remote Sensing Letters,2015,12(11):2321-2325. [107] XIAO Z F,LIU Q,TANG G F,et al.Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images[J].International Journal of Remote Sensing,2015,36(2):618-644. [108] XIA G S,HU J W,HU F,et al.AID:a benchmark data set for performance evaluation of aerial scene classification[J].IEEE Transactions on Geoscience and Remote Sensing,2017,55(7):3965-3981. [109] MAGGIORI E,TARABALKA Y,CHARPIAT G,et al.Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark[C]//Proceedings of 2017 IEEE International Geoscience and Remote Sensing Symposium.Washington D.C.,USA:IEEE Press,2017:3226-3229. [110] CHENG G,HAN J W,LU X Q.Remote sensing image scene classification:benchmark and state of the art[J].Proceedings of the IEEE,2017,105(10):1865-1883. [111] CHENG G,HAN J W,ZHOU P C,et al.Multi-class geospatial object detection and geographic image classification based on collection of part detectors[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,98:119-132. [112] ZHU H G,CHEN X G,DAI W Q,et al.Orientation robust object detection in aerial images using deep convolutional neural network[C]//Proceedings of 2015 IEEE International Conference on Image Processing.Washington D.C.,USA:IEEE Press,2015:3735-3739. [113] YANG Y,NEWSAM S.Bag-of-visual-words and spatial extensions for land-use classification[C]//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems.New York,USA:ACM Press,2010:270-279. [114] DAI D X,YANG W.Satellite image classification via two-layer sparse coding with biased image representation[J].IEEE Geoscience and Remote Sensing Letters,2011,8(1):173-176. [115] ZHU Q Q,ZHONG Y F,ZHAO B,et al.Bag-of-visual-words scene classifier with local and global features for high spatial resolution remote sensing imagery[J].IEEE Geoscience and Remote Sensing Letters,2016,13(6):747-751. [116] ZHOU W X,NEWSAM S,LI C M,et al.PatternNet:a benchmark dataset for performance evaluation of remote sensing image retrieval[J].ISPRS Journal of Photogrammetry and Remote Sensing,2018,145:197-209. [117] JIN P,XIA G S,HU F,et al.AID++:an updated version of AID on scene classification[C]//Proceedings of 2018 IEEE International Geoscience and Remote Sensing Symposium.Washington D.C.,USA:IEEE Press,2018:4721-4724. [118] SUMBUL G,CHARFUELAN M,DEMIR B,et al.BigEarthNet:a large-scale benchmark archive for remote sensing image understanding[C]//Proceedings of 2019 IEEE International Geoscience and Remote Sensing Symposium.Washington D.C.,USA:IEEE Press,2019:5901-5904. [119] SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the inception architecture for computer vision[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2016:2818-2826. [120] KIRILLOV A,HE K M,GIRSHICK R,et al.Panoptic segmentation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:9404-9413. [121] KIRILLOV A,GIRSHICK R,HE K M,et al.Panoptic feature pyramid networks[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C.,USA:IEEE Press,2019:6399-6408. [122] CHEN W L,WILSON J,TYREE S,et al.Compressing neural networks with the hashing trick[EB/OL].[2020-03-25].https://arxiv.org/abs/1504.04788. [123] RASTEGARI M,ORDONEZ V,REDMON J,et al.XNOR-net:ImageNet classification using binary convolutional neural networks[M].Berlin,Germany:Springer,2016. |
[1] | 苏炯铭, 刘鸿福, 项凤涛, 吴建宅, 袁兴生. 深度神经网络解释方法综述[J]. 计算机工程, 2020, 46(9): 1-15. |
[2] | 殷佳豪, 刘世杰, 鲍宇, 杨轩, 朱紫维. 基于一维卷积神经网络的实时心脏按压评估[J]. 计算机工程, 2020, 46(5): 298-304,311. |
[3] | 沈泽君, 丁飞飞, 杨文元. 多粒度相关滤波视频跟踪方法[J]. 计算机工程, 2020, 46(5): 274-281. |
[4] | 段大高, 梁少虎, 赵振东, 韩忠明. 基于自注意力机制的中文标点符号预测模型[J]. 计算机工程, 2020, 46(5): 291-297. |
[5] | 李南星, 盛益强, 倪宏. 用于个性化推荐的条件卷积隐因子模型[J]. 计算机工程, 2020, 46(4): 85-90,96. |
[6] | 阳萍, 谢志鹏. 基于BiLSTM模型的定义抽取方法[J]. 计算机工程, 2020, 46(3): 40-45. |
[7] | 杨晓梅, 郭文强, 张菊玲. 考虑多样性的深度神经网络结构搜索方法研究[J]. 计算机工程, 2020, 46(12): 105-112,133. |
[8] | 郑文秀, 赵峻毅, 文心怡, 姚引娣. 基于瓶颈复合特征的声学模型建立方法[J]. 计算机工程, 2020, 46(11): 301-305,314. |
[9] | 张小瑞, 陈旋, 孙伟, 葛楷. 基于深度学习的车辆再识别研究进展[J]. 计算机工程, 2020, 46(11): 1-11. |
[10] | 王重仁, 王雯, 佘杰, 凌晨. 融合深度神经网络的个人信用评估方法[J]. 计算机工程, 2020, 46(10): 308-314. |
[11] | 唐绍恩, 李骞, 胡磊, 马强, 顾大权. 一种基于迁移学习的能见度检测方法[J]. 计算机工程, 2019, 45(9): 242-247. |
[12] | 袁文浩, 梁春燕, 夏斌. 基于深度神经网络的因果形式语音增强模型[J]. 计算机工程, 2019, 45(8): 255-259. |
[13] | 盛宜华, 武友新, 姚磊岳. 一种基于YOLOv3的共享单车违规停放检测方法[J]. 计算机工程, 2019, 45(12): 237-242. |
[14] | 刘崇阳, 刘勤让. 一种神经网络模型剪枝后泛化能力的验证方法[J]. 计算机工程, 2019, 45(10): 234-238. |
[15] | 陈耀旺,严伟,俞东进,徐凯辉,夏艺,杨威. 基于深度学习的个性化网吧游戏推荐[J]. 计算机工程, 2019, 45(1): 206-209,216. |
阅读次数 | ||||||
全文 |
|
|||||
摘要 |
|
|||||
|
公众号