[1]董俊杰,刘华平,谢珺,等.基于反馈注意力机制和上下文融合的非模式实例分割[J].智能系统学报,2021,16(4):801-810.[doi:10.11992/tis.202007042]
 DONG Junjie,LIU Huaping,XIE Jun,et al.Feedback attention mechanism and context fusion based amodal instance segmentation[J].CAAI Transactions on Intelligent Systems,2021,16(4):801-810.[doi:10.11992/tis.202007042]
点击复制

基于反馈注意力机制和上下文融合的非模式实例分割

参考文献/References:
[1] SIMONYAN K, ZISSERMAN A. Very deep convolution- al networks for large-scale image recognition[EB/OL]. (20 15-04-10)[2020-07-21] https://arxiv.org/abs/1409.1556.
[2] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//Pro- ceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA, 2016: 770-778.
[3] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[C]//Proceedings of the 14th Euro- pean Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 21-37.
[4] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(6): 1137-1149.
[5] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy, 2017: 2999-3007.
[6] GIRSHICK R. Fast R-CNN[C]//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago, Chile, 2015: 1440-1448.
[7] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Pr- oceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, America, 2015: 3431-3440.
[8] CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segme- ntation[EB/OL]. (2017-10-05)[2020-07-21] https://arxiv.org/abs/1706.05587.
[9] HE Kaiming, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C]//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy, 2017: 2980-2988.
[10] BOLYA D, ZHOU Chong, XIAO Fanyi, et al. YOLACT: real-time instance segmentation[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, South Korea, 2019: 9156-9165.
[11] ZHU Yan, TIAN Yuandong, METAXAS D, et al. Semantic amodal segmentation[C]//Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, United States, 2017: 3001-3009.
[12] ZHANG Ziheng, CHEN Aapei, XIE Ling, et al. Learning semantics-aware distance map with semantics layering network for amodal instance segmentation[C]//Pro- ceedings of the 27th ACM International Conference on Multimedia. Nice, France, 2019: 2124-2132.
[13] QIAO Siyuan, CHEN L C, YUILLE A. DetectoRS: detecting objects with recursive feature pyramid and swi- tchable atrous convolution[EB/OL]. (2020-06-03)[2020-07- 21] https://arxiv.org/abs/2006.02334.
[14] LI Yi, QI Haozhi, DAI Jifeng, et al. Fully convolutional instance-aware semantic segmentation[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, United States, 2017: 4438-4446.
[15] LIU Shu, QI Lu, QIN Haifang, et al. Path aggregation network for instance segmentation[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, United States, 2018: 8759-8768.
[16] CHEN Xinlei, GIRSHICK R, HE Kaiming, et al. Tensormask: a foundation for dense object segmenta- tion[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, South Korea, 2019: 2061-2069.
[17] XIE Enze, SUN Peize, SONG Xiaoge, et al. PolarMask: single shot instance segmentation with polar representa- tion[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, United States, 2020: 12190-12199.
[18] LI Ke, MALIK J. Amodal instance segmentation[C]//Pro- ceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2016: 677-693.
[19] FOLLMANN P, K?NIG R, H?RTINGER P, et al. Learning to see the invisible: end-to-end trainable amodal instance segmentation[C]//2019 IEEE Winter Conference on Applications of Computer Vision (WACV). Waikoloa, United States, 2019: 1328-1336.
[20] EHSANI K, MOTTAGHI R, FARHADI A. SeGAN: segmenting and generating the invisible[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, United States, 2018: 6144-6153.
[21] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, United States, 2018: 7132-7141.
[22] WANG Xiaolong, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, United States, 2018: 7794-7803.
[23] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//Proceedings of the 15th Euro- pean Conference on Computer Vision (ECCV). Munich, Germany, 2018: 3-19.
[24] CAO Yun, XU Jiarui, LIN S, et al. GCNet: non-local networks meet squeeze-excitation networks and beyo- nd[C]//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshops. Seoul, South Korea, 2019: 1971-1980.
[25] FAN Zhibo, YU Jingang, LIANG Zhihao, et al. FGN: fully guided network for few-shot instance segmenta- tion[C]//Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, United States, 2020: 9169-9178.
[26] LIU Dongnan, ZHANG Donghao, SONG Yang, et al. Cell R-CNN v3: a novel panoptic paradigm for instance segmentation in biomedical images[EB/OL]. (2020-02-15) [2020-07-21] https://arxiv.org/abs/2002.06345.
[27] FU Jun, LIU Jing, TIAN Haijie, et al. Dual attention network for scene segmentation[C]//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, United States, 2019: 3141-3149.
[28] CAO Junxu, CHEN Qi, GUO Jun, et al. Attention-guided context feature pyramid network for object detecti- on[EB/OL]. (2020-05-23)[2020-07-21] https://arxiv.org/abs/2005.11475.

备注/Memo

收稿日期:2020-07-24。
基金项目:山西省自然科学基金项目(201801D121144,201801D221190);辽宁省科技厅机器人技术国家重点实验室联合基金项目(2020-KF-22-06)
作者简介:董俊杰,硕士研究生,主要研究方向为智能信息处理、计算机视觉和图像识别;刘华平,副教授,博士生导师,IEEE Senior Member、中国人工智能学会理事、中国人工智能学会认知系统与信息处理专业委员会秘书长。主要研究方向为机器人感知、学习与控制、多模态信息融合。发表学术论文340余篇;谢珺,副教授,主要研究方向为粗糙集、粒计算、数据挖掘和智能信息处理.
通讯作者:刘华平.E-mail:hpliu@tsinghua.edu.cn

更新日期/Last Update: 1900-01-01
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134