Optics and Precision Engineering, Volume. 31, Issue 19, 2884(2023)
Mask generation dynamically regulates weakly supervised video instance segmentation
[1] YANG L J, FAN Y C, XU N. Video instance segmentation[C], 5187-5196(2019).
[2] [2] 毛琳, 任凤至, 杨大伟, 等. 实例特征深度链式学习全景分割网络[J]. 光学 精密工程, 2020, 28(12):2665-2673. doi: 10.37188/ope.20202812.2665MAOL, RENF ZH, YANGD W, et al. INFNet: Deep instance feature chain learning network for panoptic segmentation[J]. Opt. Precision Eng., 2020, 28(12):2665-2673. (in Chinese). doi: 10.37188/ope.20202812.2665
[3] [3] 梁新宇, 林洗坤, 权冀川, 等. 基于深度学习的图像实例分割技术研究进展[J]. 电子学报, 2020, 48(12): 2476-2486. doi: 10.3969/j.issn.0372-2112.2020.12.025LIANGX Y, LINX K, QUANJ CH, et al. Research on the progress of image instance segmentation based on deep learning[J]. Acta Electronica Sinica, 2020, 48(12): 2476-2486.(in Chinese). doi: 10.3969/j.issn.0372-2112.2020.12.025
[4] [4] 曹天扬,蔡浩原,方东明,等. 结合图像内容匹配的机器人视觉导航定位与全局地图构建系统[J]. 光学 精密工程,2017,25(8):2221-2232. doi: 10.3788/ope.20172508.2221CAOT Y, CAIH Y, FANGD M, et al. Robot vision system for keyframe global map establishment and robot localization based on graphic content matching [J]. Opt. Precision Eng., 2017,25(8): 2221-2232. (in Chinese). doi: 10.3788/ope.20172508.2221
[5] [5] 钱夔, 宋爱国. 一种改进型机器人仿生认知神经网络[J]. 电子学报, 2015, 43(6):1084-1089. doi: 10.3969/j.issn.0372-2112.2015.06.007QIANK, SONGA G. An improved bionic cognitive neural network for robot[J]. Acta Electronica Sinica, 2015, 43(6):1084-1089.(in Chinese). doi: 10.3969/j.issn.0372-2112.2015.06.007
[6] [6] 伍锡如, 薛其威. 基于激光雷达的无人驾驶系统三维车辆检测[J]. 光学 精密工程, 2022, 30(4): 489-497. doi: 10.37188/OPE.20223004.0489WUX R, XUEQ W. 3D vehicle detection for unmanned driving systerm based on lidar[J]. Opt. Precision Eng., 2022, 30(4): 489-497.(in Chinese). doi: 10.37188/OPE.20223004.0489
[7] [7] 秦飞巍, 沈希乐, 彭勇, 等. 无人驾驶中的场景实时语义分割方法[J]. 计算机辅助设计与图形学学报, 2021, 33(7):1026-1037. doi: 10.3724/SP.J.1089.2021.18631QINF W, SHENX Y, PENGY, et al. A real-time semantic segmentation approach for autonomous driving scenes[J]. Journal of Computer-Aided Design & Computer Graphics, 2021, 33(7):1026-1037.(in Chinese). doi: 10.3724/SP.J.1089.2021.18631
[8] [8] 李淑慧, 邓志红, 冯肖雪, 等. 强杂波背景下基于变分贝叶斯推理的机载雷达目标跟踪算法[J]. 电子学报, 2022, 50(5): 1089-1097. doi: 10.12263/DZXB.20210374LISH H, DENGZH H, FENGX X, et al. Variational Bayesian Inference? Based airborne radar target tracking algorithm in strong clutter[J]. Acta Electronica Sinica, 2022, 50(5): 1089-1097. (in Chinese). doi: 10.12263/DZXB.20210374
[9] [9] 王树亮, 毕大平, 阮怀林, 等. 基于信息熵准则的认知雷达机动目标跟踪算法[J]. 电子学报, 2019, 47(6): 1277-1284. doi: 10.3969/j.issn.0372-2112.2019.06.014WANGSH L, BID P, RUANH L, et al. Cognitive radar maneuvering target tracking algorithm based on information entropy criterion[J]. Acta Electronica Sinica, 2019, 47(6): 1277-1284.(in Chinese). doi: 10.3969/j.issn.0372-2112.2019.06.014
[10] KHOREVA A, BENENSON R, HOSANG J et al. Simple does it: weakly supervised instance and semantic segmentation[C], 1665-1674(2017).
[11] [11] 任冬伟, 王旗龙, 魏云超, 等. 视觉弱监督学习研究进展[J]. 中国图象图形学报, 2022, 27(6): 1768-1798. doi: 10.11834/jig.220178REND W, WANGQ L, WEIY CH, et al. Progress in weakly supervised learning for visual understanding[J]. Journal of Image and Graphics, 2022, 27(6): 1768-1798.(in Chinese). doi: 10.11834/jig.220178
[12] LIU Q, RAMANATHAN V, MAHAJAN D et al. Weakly supervised instance segmentation for videos with temporal mask consistency[C], 13963-13973(2021).
[13] CHO S, KWAK S. Weakly supervised learning of instance segmentation with inter-pixel relations[C], 2204-2213(2020).
[14] IKEDA J, MORI J. Weakly supervised instance segmentation using motion information via optical flow[J]. arXiv preprint.
[15] TIAN Z, SHEN C H, WANG X L et al. BoxInst: high-performance instance segmentation with box annotations[C], 5439-5448(2021).
[16] MANINIS K K, PONT-TUSET J, ARBELÁEZ P et al. Convolutional oriented boundaries: from image segmentation to high-level tasks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 819-833(2018).
[17] HSU CC, HSU KJ, TSAI CC et al. Weakly supervised instance segmentation using the bounding box tightness prior[J]. Advances in Neural Information Processing Systems, 32, 6586-6597(2019).
[18] HE K M, GKIOXARI G, DOLLÁR P et al. Mask R-CNN[C], 2980-2988(2017).
[19] TIAN Z, SHEN C H, CHEN H. Conditional Convolutions for Instance Segmentation[M]. Computer Vision - ECCV 2020, 282-298(2020).
[20] BOLYA D, ZHOU C, XIAO F Y et al. YOLACT: real-time instance segmentation[C], 9156-9165(2019).
[21] CAO J L, ANWER R M, CHOLAKKAL H et al.
[22] LI M H, LI S, LI L D et al. Spatial feature calibration and temporal fusion for effective one-stage video instance segmentation[C], 11210-11219(2021).
[23] YANG S S, FANG Y X, WANG X G et al. Crossover learning for fast online video instance segmentation[C], 8023-8032(2022).
[24] HE K M, ZHANG X Y, REN S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[25] LIN T Y, DOLLÁR P, GIRSHICK R et al. Feature pyramid networks for object detection[C], 936-944(2017).
[26] SUN K, XIAO B, LIU D et al. Deep high-resolution representation learning for human pose estimation[C], 5686-5696(2020).
[28] MILLETARI F, NAVAB N, AHMADI S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C], 565-571(2016).
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Zifen HE, Lin XU, Yinhui ZHANG, Ying HUANG. Mask generation dynamically regulates weakly supervised video instance segmentation[J]. Optics and Precision Engineering, 2023, 31(19): 2884
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Received: Feb. 6, 2023
Accepted: --
Published Online: Mar. 18, 2024
The Author Email: Yinhui ZHANG (zyhhzf1998@163.com)