Computer Applications and Software, Volume. 42, Issue 4, 311(2025)
GMFNET: GLOBAL MULTI-SCALE AND MULTI-LEVEL FEATURE FUSION NETWORK FOR SEMANTIC SEGMENTATION
[1] [1] Long J, Shelhame E, Darrell T, et al. Fully convolutional networks for semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.
[2] [2] 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.
[3] [3] Yu C, Wang J, Peng C, et al. Learning a discriminative feature network for semantic segmentation[C]//2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018: 1857-1866.
[4] [4] Wang Y R, Chen Q L, Chen S L, et al. Multi-scale convolutional features network for semantic segmentation in indoor scenes[J]. IEEE Access, 2020, 8: 89575-89583.
[6] [6] Wang X L, Girshick R, Gupta A, et al. Non-local neural networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7794-7803.
[7] [7] Yuan Y H, Huang L, Guo J Y, et al. OCNet: Object context network for scene parsing[EB]. arXiv: 1809.00916, 2018.
[9] [9] Zhang B X, Li W H, Hui Y M, et al. MFENet: Multi-level feature enhancement network for real-time semantic segmentation[J]. Neurocomputing, 2020, 393: 54-65.
[11] [11] Pei Y, Sun B, Li S T. Multifeature selective fusion network for real-time driving scene parsing[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1-12.
[12] [12] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//18th International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015: 234-241.
[13] [13] Chen L C, Zhu Y K, Papandreou G, et al. Encoder-decoder with Atrous separable convolution for semantic image segmentation[C]//European Conference on Computer Vision, 2018: 801-818.
[14] [14] Wu F, Chen F, Jing X Y, et al. Dynamic attention network for semantic segmentation[J]. Neurocomputing, 2020, 384: 182-191.
[15] [15] Yu F, Koltun V. Multi-scale context aggregation by dilated convolutions[EB]. arXiv: 1511.07122, 2015.
[16] [16] 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.
[17] [17] Chen L C, Papandreou G, Schroff F, et al. Rethinking Atrous convolution for semantic image segmentation[EB]. arXiv: 1706.05587, 2017.
[18] [18] Zhao H S, Shi J P, Qi X J, et al. Pyramid scene parsing network[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2881-2890.
[19] [19] Yang M K, Yu K, Zhang C, et al. DenseASPP for semantic segmentation in street scenes[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2018: 3684-3692.
[20] [20] Liu W, Rabinovich A, Berg A C. ParseNet: Looking wider to see better[EB]. arXiv: 1506.04579, 2015.
[21] [21] Zhang H, Dana K, Shi J, et al. Context encoding for semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7151-7160.
[22] [22] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[23] [23] Li H C, Xiong P F, An J, et al. Pyramid attention network for semantic segmentation[EB]. arXiv: 1805.10180, 2018.
[24] [24] Cui B G, Jing W, Huang L, et al. Sanet: A sea-land segmentation network via adaptive multiscale feature learning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 14: 116-126.
[25] [25] Fu J, Liu J, Tian H J, et al. Dual attention network for scene segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2019: 3146-3154.
[26] [26] Zhang H, Zhang H, Wang C G, et al. Co-occurrent features in semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2019: 548-557.
[28] [28] Wu Y, Huang Z M, Long H Y, et al. A semantic segmentation network simulating the ventral and dorsal pathways of the cerebral visual cortex[J]. IEEE Access, 2021, 9: 47230- 47242.
Get Citation
Copy Citation Text
Chen Jinling, Zhao Chengming, Li Jie. GMFNET: GLOBAL MULTI-SCALE AND MULTI-LEVEL FEATURE FUSION NETWORK FOR SEMANTIC SEGMENTATION[J]. Computer Applications and Software, 2025, 42(4): 311
Category:
Received: Dec. 15, 2021
Accepted: Aug. 25, 2025
Published Online: Aug. 25, 2025
The Author Email: