Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (2): 99-106.doi: 10.6040/j.issn.1672-3961.0.2021.329
YIN Xu1, LIU Zhaoying1, ZHANG Ting1*, LI Yujian1,2
CLC Number:
[1] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2015: 3431-3440. [2] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C] //Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham, Switzerland: Springer, 2015: 234-241. [3] 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. [4] LIN G, MILAN A, SHEN C, et al. Refinenet: multi-path refinement networks for high-resolution semantic segmentation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 1925-1934. [5] 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, 2017, 40(4): 834-848. [6] ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 2881-2890. [7] CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C] //Proceedings of the European Conference on Computer Vision. Cham, Switzerland: Spri-nger, 2018: 801-818. [8] 陈倩. 基于局部区域生长和Faster R-CNN的弱监督图像语义分割[D].合肥: 安徽大学, 2020. CHEN Qian. Weakly supervised image semantic segmentation based on local region growth and faster R-CNN[D]. Hefei: Anhui University, 2020. [9] ZHOU B, KHOSLA A, LAPEDRIZA A, et al. Learning deep features for discriminative localization[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 2921-2929. [10] SINGH K K, LEE Y J. Hide-and-seek: forcing a network to be meticulous for weakly-supervised object and action localization[C] //Proceedings of the 2017 IEEE International Conference on Computer Vision. New York, USA: IEEE, 2017: 3544-3553. [11] WEI Y, FENG J, LIANG X, et al. Object region mining with adversarial erasing: a simple classification to semantic segmentation approach[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 1568-1576. [12] MAI J, YANG M, LUO W. Erasing integrated learning: a simple yet effective approach for weakly supervised object localization[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2020: 8766-8775. [13] ZHANG X, WEI Y, FENG J, et al. Adversarial complementary learning for weakly supervised object localization[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 1325-1334. [14] HOU Q, JIANG P T, WEI Y, et al. Self-erasing network for integral object attention[J]. Advances in Neural Information Processing Systems, 2018, 31: 549-559. [15] KIM D, CHO D, YOO D, et al. Two-phase learning for weakly supervised object localization[C] //Proceedings of the IEEE International Conference on Computer Vision. New York, USA: IEEE, 2017: 3534-3543 [16] WANG X, SHRIVASTAVA A, GUPTA A. A-fast-rcnn: hard positive generation via adversary for object detection[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2017: 2606-2615. [17] WEI Y, XIAO H, SHI H, et al. Revisiting dilated convolution: a simple approach for weakly- and semi-supervised semantic segmentation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 7268-7277. [18] FAN J, ZHANG Z, TAN T, et al. Cian: cross-image affinity net for weakly supervised semantic segmentation[C] //Proceedings of the AAAI Conference on Arti-ficial Intelligence. Palo Alto, USA: AAAI, 2020, 10762-10769. [19] FAN R, HOU Q, CHENG M M, et al. Associating inter-image salient instances for weakly supervised semantic segmentation[C] //Proceedings of the European Conference on Computer Vision. Berlin, Germany: Springer, 2018: 367-383. [20] KOLESNIKOV A, LAMPERT C H. Seed, expand and constrain: three principles for weakly-supervised image segmentation[C] //Proceedings of the European Conference on Computer Vision. Cham, Switzerland: Springer, 2016: 695-711. [21] HUANG Z, WANG X, WANG J, et al. Weakly-supervised semantic segmentation network with deep seeded region growing[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 7014-7023. [22] WANG X, YOU S, LI X, et al. Weakly-supervised semantic segmentation by iteratively mining common object features[C] //Proceedings of the IEEE Confe-rence on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 1354-1362. [23] AHN J, KWAK S. Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2018: 4981-4990. [24] SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-cam: visual explanations from deep networks via gradient-based localization[C] //Proceedings of the IEEE International Conference on Computer Vision. New York, USA: IEEE, 2017: 618-626. [25] SOULY N, SPAMPINATO C, SHAH M. Semi supervised semantic segmentation using generative adversarial network[C] //Proceedings of the IEEE International Conference on Computer Vision. New York, USA: IEEE, 2017: 5688-5696. [26] AI J, CHEN S, DENG P, et al. CycleGANs for semi-supervised defects segmentation[C] //Proceedings of the 2020 International Conference on Sensing, Measurement & Data Analytics in the Era of Artificial Intel-ligence(ICSMD). Xi'an, China: IEEE, 2020: 611-616. [27] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C] //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2016: 770-778. [28] WU Z, SU L, HUANG Q. Cascaded partial decoder for fast and accurate salient object detection[C] //Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2019: 3907-3916. [29] KINGMA D, BA J. Adam: a method for stochastic optimization[C] //Proceedings of the 3rd International Conference on Learning Representations. CA, USA: ICLR, 2015: 1-13. [30] KRÄHENB(¨overU)HL P, KOLTUN V. Efficient inference in fully connected crfs with gaussian edge potentials[J]. Advances in Neural Information Processing Systems, 2011, 24(24): 109-117. |
[1] | Tongyu JIANG,Fan CHEN,Hongjie HE. Lightweight face super-resolution network based on asymmetric U-pyramid reconstruction [J]. Journal of Shandong University(Engineering Science), 2022, 52(1): 1-8, 18. |
[2] | Jun HU,Dongmei YANG,Li LIU,Fujin ZHONG. Cross social network user alignment via fusing node state information [J]. Journal of Shandong University(Engineering Science), 2021, 51(6): 49-58. |
[3] | Ye LIANG,Nan MA,Hongzhe LIU. Image-dependent fusion method for saliency maps [J]. Journal of Shandong University(Engineering Science), 2021, 51(4): 1-7. |
[4] | Xinlu ZONG,Jiayuan DU. Evacuation simulation model based on multi-target driven artificial bee colony algorithm [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 1-6. |
[5] | WU Zhenpeng, ZHANG Jian, FAN Xingqi, LI Cuiping. Median calculation algorithms based on GPU in OLAP [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 7-14. |
[6] | YANG Xiuyuan, PENG Tao, YANG Liang, LIN Hongfei. Adaptive multi-domain sentiment analysis based on knowledge distillation [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 15-21. |
[7] | FU Guixia, ZOU Guofeng, MAO Shuai, PAN Jinfeng, YIN Liju. Small sample person re-identification combining Gabor features and convolution features [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 22-29. |
[8] | TAO Liang, LIU Baoning, LIANG Wei. Automatic detection research of arrhythmia based on CNN-LSTM hybrid model [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 30-36. |
[9] | Junsan ZHANG,Qiaoqiao CHENG,Yao WAN,Jie ZHU,Shidong ZHANG. MIRGAN: a medical image report generation model based on GAN [J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 9-18. |
[10] | Hui HE,Junhao HUANG. Eye tracking in human-computer interaction control [J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 1-8. |
[11] | Hao XIAO,Zhuhua LIAO,Yizhi LIU,Silin LIU,Jianxun LIU. Unmanned vehicle path planning based on deep Q learning in real environment [J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 100-107. |
[12] | Fengyu ZHOU,Panlong GU,Fang WAN,Lei YIN,Jiakai HE. Overview of multi-motion vision odometer [J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 1-10. |
[13] | WU Huihong, QIAN Shuqu, LIU Yanmin, XU Guofeng, GUO Benhua. Multiobjective dynamic economic emission dispatch differential evolution algorithm based on elites cloning local search [J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 11-23. |
[14] | WANG Mei, XUE Chenglong, ZHANG Qiang. Multi-kernel combination method based on rank spatial difference [J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 108-113. |
[15] | Zhuoyu XIAO,Pei HE,Guo CHEN,Yunbiao XU,Jie GUO. Design pattern classification mining with feature metrics constraints [J]. Journal of Shandong University(Engineering Science), 2020, 50(6): 48-58. |
|