参考文献/References:
[1] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(4):640.
[2] HOPFIELD J J. Neural networks and physical systems with emergent collective computational abilities[J]. Proceedings of the National Academy of Sciences,1982,79:2554.
[3] 姬壮伟.基于深度全卷积神经网络的图像识别研究[J].山西大同大学学报(自然科学版),2022,38(2):27.
[4] HAZIRBAS C, MA L N, DOMOKOS C, et al. FuseNet:incorporating depth into semantic segmentation via fusion-based CNN architecture[C]//Asian Conference on Computer Vision. Taipei:Springer,2017:213.
[5] YANG E, ZHOU W J, QIAN X H, et al. MGCNet:multilevel gated collaborative network for RGB-D semantic segmentation of indoor scene[J]. IEEE Signal Processing Letters,2022,29:2567.
[6] WU P, GUO R Z, TONG X Z, et al. Link-RGBD:cross-guided feature fusion network for RGBD semantic segmentation[J]. IEEE Sensors Journal,2022,22(24):24161.
[7] JIANG J D, ZHENG L N, LUO F, et al. RedNet:residual encoder-decoder network for indoor RGB-D semantic segmentation[EB/OL].(2018-08-06)[2023-10-25]. https://arxiv.org/abs/1806.01054.
[8] CHEN L Z, LIN Z, WANG Z Q, et al. Spatial information guided convolution for real-time RGBD semantic segmentation[J]. IEEE Transactions on Image Processing, 2021,30:2313.
[9] CAO J M, LENG H C, LISCHINSKI D, et al. ShapeConv:shape-aware convolutional layer for indoor RGB-D semantic segmentation[C]//Proceedings of the IEEE International Conference on Computer Vision. Montreal:IEEE, 2021:7088.
[10] ZHOU W J, YANG E Q, LEI J S, et al. PGDENet:progressive guided fusion and depth enhancement network for RGB-D indoor scene parsing[J]. IEEE Transactions on Multimedia,2022,25:3483.
[11] CHEN X K, LIN K Y, WANG J B, et al. Bi-directional cross-modality feature propagation with separation-and-aggregation gate for RGB-D semantic segmentation[C]//European Conference on Computer Vision. Glasgow:Springer,2020:561.
[12] ZHOU W, YUE Y, FANG M, et al. BCINet:bilateral cross-modal interaction network for indoor scene understanding in RGB-D images[J]. Information Fusion,2023,94:32.
[13] 徐高,周武杰,叶绿.基于边界-图卷积的机器人行驶路障场景解析[J].浙江科技学院学报,2023,35(5):402.
[14] 李成豪,张静,胡莉,等.基于多尺度感受野融合的小目标检测算法[J].计算机工程与应用,2022,58(12):177.
[15] ZHANG L F, BAO C L, MA K. Self-distillation:owards efficient and compact neural networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2022,44(8):4388.
[16] 郑云飞,王晓兵,张雄伟,等.基于金字塔知识的自蒸馏HRNet目标分割方法[J].电子学报,2023,51(3):746.
[17] AN S, LIAO Q M, LU Z Q, et al. Efficient semantic segmentation via self-attention and self-distillation[J]. IEEE Transactions on Intelligent Transportation Systems,2022,23(9):15256.
[18] XIE E Z, WANG W H, YU Z D, et al. SegFormer:simple and efficient design for semantic segmentation with transformers[J]. Advances in Neural Information Processing Systems,2021,34:12077.
[19] WANG Y K, HUANG W B, SUN F C, et al. Deep multimodal fusion by channel exchanging[C]//Conferences on Neural Information Processing Systems. Vancouver:NeurIPS, 2020:4835.
[20] HINTON G, VINYALS O, DEAN J. Distilling the knowledge in a neural network[EB/OL].(2015-03-09)[2023-10-25]. https://arxiv.org/abs/1503.02531.
[21] MILLETARI F, NAVAB N, AHMADI S A. V-Net:fully convolutional neural networks for volumetric medical image segmentation[C]//2016 Fourth International Conference on 3D Vision. Stanford:IEEE,2016:565.
[22] SILBERMAN N, HOIEM D, KOHLI P, et al. Indoor segmentation and support inference from rgbd images[C]//Conference on Computer Vision. Florence:Springer,2012:746.
[23] XIAO J X, OWENS A, TORRALBA A. Sun3D:a database of big spaces reconstructed using SfM and object labels[C]//International Conference on Computer Vision. Sydney:IEEE,2013:1625.