Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (5): 1471-1478.DOI: 10.11772/j.issn.1001-9081.2023050802
Special Issue: 第十九届中国机器学习会议(CCML 2023)
• The 19th China Conference on Machine Learning (CCML 2023) • Previous Articles Next Articles
Received:
2023-06-25
Revised:
2023-07-21
Accepted:
2023-08-02
Online:
2023-08-07
Published:
2024-05-10
Contact:
Xiaoyun CHEN
About author:
GAO Wenshuo, born in 1999, M. S. candidate. His research interests include machine learning, point cloud classification.
Supported by:
通讯作者:
陈晓云
作者简介:
高文烁(1999—),男,山东济南人,硕士研究生,CCF会员,主要研究方向:机器学习、点云分类基金资助:
CLC Number:
Wenshuo GAO, Xiaoyun CHEN. Point cloud classification network based on node structure[J]. Journal of Computer Applications, 2024, 44(5): 1471-1478.
高文烁, 陈晓云. 基于节点结构的点云分类网络[J]. 《计算机应用》唯一官方网站, 2024, 44(5): 1471-1478.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023050802
方法 | 输入 | mAcc/% | OA/% | 参数量/106 |
---|---|---|---|---|
PointNet[ | 1 024 ps | 63.4 | 68.2 | 3.47 |
PointNet++[ | 1 024 ps | 75.4 | 77.9 | 1.74 |
SpiderCNN[ | 1 024 ps | 69.8 | 73.7 | — |
DGCNN[ | 1 024 ps | 73.6 | 78.1 | 1.81 |
MVTN[ | 12 vs | — | 82.8 | 4.24 |
PRA-Net[ | 1 024 ps | 79.1 | 82.1 | 2.3 |
Point-BERT[ | 1 024 ps | — | 83.1 | — |
Point-TnT[ | 1 024 ps | 81.0 | 83.5 | 3.9 |
SageMix[ | 1 024 ps | — | 83.6 | — |
Point-MAE[ | 1 024 ps | — | 85.2 | — |
PointMLP[ | 1 024 ps | 84.4 | 85.7 | 12.6 |
RepSurf-U[ | 1 024 ps | — | 86.0 | 6.8 |
NsNet-3-1 | 1 024 ps | 83.7 | 85.5 | 2.4 |
NsNet-4-1 | 1 024 ps | 85.0 | 87.0 | 8.0 |
Tab. 1 Classification accuracy and parameter quantity comparison of different methods on ScanObjectNN dataset
方法 | 输入 | mAcc/% | OA/% | 参数量/106 |
---|---|---|---|---|
PointNet[ | 1 024 ps | 63.4 | 68.2 | 3.47 |
PointNet++[ | 1 024 ps | 75.4 | 77.9 | 1.74 |
SpiderCNN[ | 1 024 ps | 69.8 | 73.7 | — |
DGCNN[ | 1 024 ps | 73.6 | 78.1 | 1.81 |
MVTN[ | 12 vs | — | 82.8 | 4.24 |
PRA-Net[ | 1 024 ps | 79.1 | 82.1 | 2.3 |
Point-BERT[ | 1 024 ps | — | 83.1 | — |
Point-TnT[ | 1 024 ps | 81.0 | 83.5 | 3.9 |
SageMix[ | 1 024 ps | — | 83.6 | — |
Point-MAE[ | 1 024 ps | — | 85.2 | — |
PointMLP[ | 1 024 ps | 84.4 | 85.7 | 12.6 |
RepSurf-U[ | 1 024 ps | — | 86.0 | 6.8 |
NsNet-3-1 | 1 024 ps | 83.7 | 85.5 | 2.4 |
NsNet-4-1 | 1 024 ps | 85.0 | 87.0 | 8.0 |
方法 | 数据输入 | OA/% | 方法 | 数据输入 | OA/% |
---|---|---|---|---|---|
PointNet[ | 1 024 ps | 89.2 | DensePoint[ | 1 024 ps | 93.2 |
PointNet++[ | 1 024 ps | 90.7 | PointASNL[ | 1 024 ps | 92.9 |
PointNet++[ | 5 000 ps+n | 91.9 | PCT[ | 1 024 ps | 93.2 |
PointConv[ | 1 024 ps+n | 92.5 | MLMSPT[ | 1 024 ps | 92.9 |
KPConv[ | 6 800 ps | 92.9 | PointStack[ | 1 024 ps | 93.3 |
DGCNN[ | 1 024 ps | 92.9 | Point-TnT[ | 1 024 ps | 92.6 |
RS-CNN[ | 1 024 ps | 92.9 | NsNet-4-1 | 1 024 ps | 93.6 |
Tab. 2 OA comparison of different methods on ModelNet40 dataset
方法 | 数据输入 | OA/% | 方法 | 数据输入 | OA/% |
---|---|---|---|---|---|
PointNet[ | 1 024 ps | 89.2 | DensePoint[ | 1 024 ps | 93.2 |
PointNet++[ | 1 024 ps | 90.7 | PointASNL[ | 1 024 ps | 92.9 |
PointNet++[ | 5 000 ps+n | 91.9 | PCT[ | 1 024 ps | 93.2 |
PointConv[ | 1 024 ps+n | 92.5 | MLMSPT[ | 1 024 ps | 92.9 |
KPConv[ | 6 800 ps | 92.9 | PointStack[ | 1 024 ps | 93.3 |
DGCNN[ | 1 024 ps | 92.9 | Point-TnT[ | 1 024 ps | 92.6 |
RS-CNN[ | 1 024 ps | 92.9 | NsNet-4-1 | 1 024 ps | 93.6 |
方法 | Inst.mIoU | Cls.mIoU |
---|---|---|
PointNet[ | 83.7 | 80.4 |
PointNet++[ | 85.1 | 81.9 |
SpiderCNN[ | 85.3 | 82.4 |
DGCNN[ | 85.2 | 82.3 |
SynSpec[ | 84.7 | 82.0 |
SPLATNet[ | 85.4 | 83.7 |
NsNet-4-1 | 85.8 | 83.6 |
Tab. 3 Cls.mIoU and Inst.mIoU comparison of different methods on ShapeNet-Part dataset
方法 | Inst.mIoU | Cls.mIoU |
---|---|---|
PointNet[ | 83.7 | 80.4 |
PointNet++[ | 85.1 | 81.9 |
SpiderCNN[ | 85.3 | 82.4 |
DGCNN[ | 85.2 | 82.3 |
SynSpec[ | 84.7 | 82.0 |
SPLATNet[ | 85.4 | 83.7 |
NsNet-4-1 | 85.8 | 83.6 |
方法 | 分类交并比/% | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
air. | bag | cap | car | cha. | ear. | gui. | kni. | lam. | lap. | mot. | mug. | pis. | roc. | sta. | tab. | |
PointNet[ | 83.4 | 78.7 | 82.5 | 74.9 | 89.6 | 73.0 | 91.5 | 85.9 | 80.8 | 95.3 | 65.2 | 93.0 | 81.2 | 57.9 | 72.8 | 80.6 |
PointNet++[ | 82.4 | 79.0 | 87.7 | 77.3 | 90.8 | 71.8 | 91.0 | 85.9 | 83.7 | 95.3 | 71.6 | 94.1 | 81.3 | 58.7 | 76.4 | 82.6 |
SpiderCNN[ | 83.5 | 81.0 | 87.2 | 77.5 | 90.7 | 76.8 | 91.1 | 87.3 | 83.3 | 95.8 | 70.2 | 93.5 | 82.7 | 59.7 | 75.8 | 82.8 |
DGCNN[ | 84.0 | 83.4 | 86.7 | 77.8 | 90.6 | 74.7 | 91.2 | 87.5 | 82.8 | 95.7 | 66.3 | 94.9 | 81.1 | 63.5 | 74.5 | 82.6 |
SynSpec[ | 81.6 | 81.7 | 81.9 | 75.2 | 90.2 | 74.9 | 93.0 | 86.1 | 84.7 | 95.6 | 66.7 | 92.7 | 81.6 | 60.6 | 82.9 | 82.1 |
SPLATNet[ | 83.2 | 84.3 | 89.1 | 80.3 | 90.7 | 75.5 | 92.1 | 87.1 | 83.9 | 96.3 | 75.6 | 95.8 | 83.8 | 64.0 | 75.5 | 81.8 |
NsNet-4-1 | 84.3 | 80.9 | 86.1 | 80.0 | 90.4 | 73.4 | 91.7 | 86.8 | 81.6 | 96.2 | 77.7 | 94.7 | 84.0 | 64.5 | 81.6 | 83.9 |
Tab. 4 Class mean IoU of segmentation results of different methods for ShapeNet-Part dataset
方法 | 分类交并比/% | |||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
air. | bag | cap | car | cha. | ear. | gui. | kni. | lam. | lap. | mot. | mug. | pis. | roc. | sta. | tab. | |
PointNet[ | 83.4 | 78.7 | 82.5 | 74.9 | 89.6 | 73.0 | 91.5 | 85.9 | 80.8 | 95.3 | 65.2 | 93.0 | 81.2 | 57.9 | 72.8 | 80.6 |
PointNet++[ | 82.4 | 79.0 | 87.7 | 77.3 | 90.8 | 71.8 | 91.0 | 85.9 | 83.7 | 95.3 | 71.6 | 94.1 | 81.3 | 58.7 | 76.4 | 82.6 |
SpiderCNN[ | 83.5 | 81.0 | 87.2 | 77.5 | 90.7 | 76.8 | 91.1 | 87.3 | 83.3 | 95.8 | 70.2 | 93.5 | 82.7 | 59.7 | 75.8 | 82.8 |
DGCNN[ | 84.0 | 83.4 | 86.7 | 77.8 | 90.6 | 74.7 | 91.2 | 87.5 | 82.8 | 95.7 | 66.3 | 94.9 | 81.1 | 63.5 | 74.5 | 82.6 |
SynSpec[ | 81.6 | 81.7 | 81.9 | 75.2 | 90.2 | 74.9 | 93.0 | 86.1 | 84.7 | 95.6 | 66.7 | 92.7 | 81.6 | 60.6 | 82.9 | 82.1 |
SPLATNet[ | 83.2 | 84.3 | 89.1 | 80.3 | 90.7 | 75.5 | 92.1 | 87.1 | 83.9 | 96.3 | 75.6 | 95.8 | 83.8 | 64.0 | 75.5 | 81.8 |
NsNet-4-1 | 84.3 | 80.9 | 86.1 | 80.0 | 90.4 | 73.4 | 91.7 | 86.8 | 81.6 | 96.2 | 77.7 | 94.7 | 84.0 | 64.5 | 81.6 | 83.9 |
网络 | m | n | TrainOA/% | TestmAcc/% | TestOA/% |
---|---|---|---|---|---|
NsNet-1-1 | 1 | 1 | 91.778 | 74.8 | 77.7 |
NsNet-2-1 | 2 | 1 | 99.798 | 82.0 | 83.7 |
NsNet-3-1 | 3 | 1 | 100.000 | 83.7 | 85.5 |
NsNet-4-1 | 4 | 1 | 100.000 | 85.0 | 87.0 |
NsNet-5-1 | 5 | 1 | 100.000 | 85.2 | 86.5 |
NsNet-6-1 | 6 | 1 | 99.912 | 83.0 | 85.1 |
NsNet-1-2 | 1 | 2 | 92.796 | 76.1 | 78.3 |
NsNet-2-2 | 2 | 2 | 99.867 | 81.8 | 83.5 |
NsNet-3-2 | 3 | 2 | 99.908 | 84.1 | 85.4 |
NsNet-4-2 | 4 | 2 | 99.930 | 85.2 | 86.2 |
NsNet-5-2 | 5 | 2 | 99.700 | 84.5 | 86.0 |
Tab.5 Comparison experiment results about NsNet layer change on ScanbObjectNN dataset
网络 | m | n | TrainOA/% | TestmAcc/% | TestOA/% |
---|---|---|---|---|---|
NsNet-1-1 | 1 | 1 | 91.778 | 74.8 | 77.7 |
NsNet-2-1 | 2 | 1 | 99.798 | 82.0 | 83.7 |
NsNet-3-1 | 3 | 1 | 100.000 | 83.7 | 85.5 |
NsNet-4-1 | 4 | 1 | 100.000 | 85.0 | 87.0 |
NsNet-5-1 | 5 | 1 | 100.000 | 85.2 | 86.5 |
NsNet-6-1 | 6 | 1 | 99.912 | 83.0 | 85.1 |
NsNet-1-2 | 1 | 2 | 92.796 | 76.1 | 78.3 |
NsNet-2-2 | 2 | 2 | 99.867 | 81.8 | 83.5 |
NsNet-3-2 | 3 | 2 | 99.908 | 84.1 | 85.4 |
NsNet-4-2 | 4 | 2 | 99.930 | 85.2 | 86.2 |
NsNet-5-2 | 5 | 2 | 99.700 | 84.5 | 86.0 |
密度加权 | 球形坐标 | 平滑交叉熵 | mAcc | OA |
---|---|---|---|---|
卷积密度 | 85.0 | 87.0 | ||
— | 78.1 | 81.1 | ||
高斯密度 | 83.2 | 86.0 | ||
卷积密度 | — | 84.9 | 86.7 | |
卷积密度 | 卷积网络* | 84.8 | 86.7 | |
卷积密度 | 交叉熵* | 83.2 | 85.6 |
Tab.6 Ablation experimental results on ScanbObjectNN dataset
密度加权 | 球形坐标 | 平滑交叉熵 | mAcc | OA |
---|---|---|---|---|
卷积密度 | 85.0 | 87.0 | ||
— | 78.1 | 81.1 | ||
高斯密度 | 83.2 | 86.0 | ||
卷积密度 | — | 84.9 | 86.7 | |
卷积密度 | 卷积网络* | 84.8 | 86.7 | |
卷积密度 | 交叉熵* | 83.2 | 85.6 |
1 | WU Z, SONG S, KHOSLA A, et al. 3D ShapeNets: a deep representation for volumetric shapes[C]// Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2015: 1912-1920. 10.1109/cvpr.2015.7298801 |
2 | FENG Y, ZHANG Z, ZHAO X,et al. GVCNN:group-view convolutional neural networks for 3D shape recognition[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 264-272. 10.1109/cvpr.2018.00035 |
3 | YI L, SU H, GUO X, et al. SyncSpecCNN: synchronized spectral cnn for 3D shape segmentation[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 6584-6592. 10.1109/cvpr.2017.697 |
4 | SIMONOVSKY M, KOMODAKIS N. Dynamic edge-conditioned filters in convolutional neural networks on graphs[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 29-38. 10.1109/cvpr.2017.11 |
5 | RIEGLER G, ULUSOY A O, GEIGER A. OctNet: learning deep 3D representations at high resolutions[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 6620-6629. 10.1109/cvpr.2017.701 |
6 | MATURANA D, SCHERER S. VoxNet: a 3D convolutional neural network for real-time object recognition[C]// Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway: IEEE, 2015: 922-928. 10.1109/iros.2015.7353481 |
7 | WU J, ZHANG C, XUE T, et al. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling[C]// Proceedings of the 30th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2016: 82-90. |
8 | VOSSLMAN G, GORTE B G H, SITHOLE G, et al. Recognising structure in laser scanner point clouds[C]// Proceedings of the 2004 International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences. Amsterdam: Elsevier, 2004: 33-38. |
9 | CHEN C, LI G, XU R, et al. ClusterNet: deep hierarchical cluster network with rigorously rotation-invariant representation for point cloud analysis[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 4989-4997. 10.1109/cvpr.2019.00513 |
10 | LIANG Z, YANG M, DENG L, et al. Hierarchical depthwise graph convolutional neural network for 3D semantic segmentation of point clouds[C]// Proceedings of the 2019 International Conference on Robotics and Automation. Piscataway: IEEE, 2019: 8152-8158. 10.1109/icra.2019.8794052 |
11 | YAN X, ZHENG C, LI Z, et al. PointASNL: robust point clouds processing using nonlocal neural networks with adaptive sampling[C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 5588-5597. 10.1109/cvpr42600.2020.00563 |
12 | ZHAO H, JIANG L, JIA J, et al. Point transformer[C]// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 16239-16248. 10.1109/iccv48922.2021.01595 |
13 | QI C R, SU H, MO K, et al. PointNet: deep learning on point sets for 3D classification and segmentation[C]// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017: 77-85. 10.1109/cvpr.2017.16 |
14 | QI C R, YI L, SU H, et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 5105-5114. |
15 | WU W, QI Z, LI F. PointConv: deep convolutional networks on 3D point clouds[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018: 9621-9630. 10.1109/cvpr.2019.00985 |
16 | XU Y, FAN T, XU M, et al. SpiderCNN: deep learning on point sets with parameterized convolutional filters[C]// Proceedings of the 15th European Conference on Computer Vision. Berlin: Springer, 2018: 90-105. 10.1007/978-3-030-01237-3_6 |
17 | HAMDI A, GIANCOLA S, LI B, et al. MVTN: multi-view transformation network for 3D shape recognition[C]// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 1-11. 10.1109/iccv48922.2021.00007 |
18 | YU X, TANG L, RAO Y, et al. Point-BERT: pre-training 3D point cloud transformers with masked point modeling[C]// Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 19291-19300. 10.1109/cvpr52688.2022.01871 |
19 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2017: 6000-6010. |
20 | THOMAS H, QI C R, J-E DESCHAUD, et al. KPConv: flexible and deformable convolution for point clouds[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 6410-6419. 10.1109/iccv.2019.00651 |
21 | MA X, QIN C, YOU H, et al. Rethinking network design and local geometry in point cloud: a simple residual MLP framework [C/OL]// Proceedings of the 10th International Conference on Learning Representations. [S.l.]: ICLR, 2022 [2023-05-01]. . |
22 | LEE S, JEON M, KIM I, et al. SageMix: saliency-guided mixup for point clouds [C/OL]// Proceedings of the 36th Annual Conference on Neural Information Processing Systems. [S.l.]: NeurIPS, 2022 [2023-05-01]. . |
23 | BERG A, OSKARSSON M, O’CONNOR M. Points to patches: enabling the use of self-attention for 3D shape recognition[C]// Proceedings of the 2022 26th International Conference on Pattern Recognition. Piscataway: IEEE, 2022: 528-534. 10.1109/icpr56361.2022.9956172 |
24 | CHENG S, CHEN X, HE X, et al. PRA-Net: point relation-aware network for 3D point cloud analysis[J]. IEEE Transactions on Image Processing, 2021, 30: 4436-4448. 10.1109/tip.2021.3072214 |
25 | GUO M-H, CAI J-X, LIU Z-N, et al. PCT: point cloud Transformer[J]. Computational Visual Media, 2021, 7: 187-199. 10.1007/s41095-021-0229-5 |
26 | RAN H, LIU J, WANG C. Surface representation for point clouds[C]// Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2022: 18920-18930. 10.1109/cvpr52688.2022.01837 |
27 | LIU Y, FAN B, XIANG S, et al. Relation-shape convolutional neural network for point cloud analysis[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 8895-8904. 10.1109/cvpr.2019.00910 |
28 | HAN X-F, KUANG Y-J, XIAO G-Q. Point cloud learning with transformer [EB/OL]. (2021-04-28) [2022-08-25]. . 10.21203/rs.3.rs-2200447/v1 |
29 | WIJAYA K T, D-H PAEK, KONG S-H. Advanced feature learning on point clouds using multi-resolution features and learnable pooling [EB/OL]. (2022-05-20) [2023-05-20]. . |
30 | UY M A, Q-G PHAM, HUA B-S, et al. Revisiting point cloud classification: a new benchmark dataset and classification model on real-world data[C]// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 1588-1597. 10.1109/iccv.2019.00167 |
31 | LIU Y, FAN B, MENG G, et al. DensePoint: learning densely contextual representation for efficient point cloud processing[C]// Proceedings of the 2019 IEEE International Conference on Computer Vision. Piscataway: IEEE, 2019: 5239-5248. 10.1109/iccv.2019.00534 |
32 | FAN S, DONG Q, ZHU Y, et al. SCF-Net: learning spatial contextual features for large-scale point cloud segmentation[C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 14504-14513. 10.1109/cvpr46437.2021.01427 |
33 | PANG Y, WANG W, TAY F E H, et al. Masked autoencoders for point cloud self-supervised learning[C]// Proceedings of the 17th European Conference on Computer Vision. Cham: Springer, 2022: 604-621. 10.1007/978-3-031-20086-1_35 |
34 | MÜLLER R, KORNBLITH S, HINTON G. When does label smoothing help?[C]// Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook: Curran Associates Inc., 2019: 4694-4703. 10.48550/arXiv.1906.02629 |
35 | SU H, JAMPANI V, SUN D, et al. SPLATNet: sparse lattice networks for point cloud processing[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway: IEEE, 2018: 2530-2539. 10.1109/cvpr.2018.00268 |
[1] | Yun LI, Fuyou WANG, Peiguang JING, Su WANG, Ao XIAO. Uncertainty-based frame associated short video event detection method [J]. Journal of Computer Applications, 2024, 44(9): 2903-2910. |
[2] | Hong CHEN, Bing QI, Haibo JIN, Cong WU, Li’ang ZHANG. Class-imbalanced traffic abnormal detection based on 1D-CNN and BiGRU [J]. Journal of Computer Applications, 2024, 44(8): 2493-2499. |
[3] | Yangyi GAO, Tao LEI, Xiaogang DU, Suiyong LI, Yingbo WANG, Chongdan MIN. Crowd counting and locating method based on pixel distance map and four-dimensional dynamic convolutional network [J]. Journal of Computer Applications, 2024, 44(7): 2233-2242. |
[4] | Dongwei WANG, Baichen LIU, Zhi HAN, Yanmei WANG, Yandong TANG. Deep network compression method based on low-rank decomposition and vector quantization [J]. Journal of Computer Applications, 2024, 44(7): 1987-1994. |
[5] | Mengyuan HUANG, Kan CHANG, Mingyang LING, Xinjie WEI, Tuanfa QIN. Progressive enhancement algorithm for low-light images based on layer guidance [J]. Journal of Computer Applications, 2024, 44(6): 1911-1919. |
[6] | Jianjing LI, Guanfeng LI, Feizhou QIN, Weijun LI. Multi-relation approximate reasoning model based on uncertain knowledge graph embedding [J]. Journal of Computer Applications, 2024, 44(6): 1751-1759. |
[7] | Min SUN, Qian CHENG, Xining DING. CBAM-CGRU-SVM based malware detection method for Android [J]. Journal of Computer Applications, 2024, 44(5): 1539-1545. |
[8] | Pengfei ZHANG, Litao HAN, Hengjian FENG, Hongmei LI. Point cloud semantic segmentation based on attention mechanism and global feature optimization [J]. Journal of Computer Applications, 2024, 44(4): 1086-1092. |
[9] | Jie WANG, Hua MENG. Image classification algorithm based on overall topological structure of point cloud [J]. Journal of Computer Applications, 2024, 44(4): 1107-1113. |
[10] | Tianhua CHEN, Jiaxuan ZHU, Jie YIN. Bird recognition algorithm based on attention mechanism [J]. Journal of Computer Applications, 2024, 44(4): 1114-1120. |
[11] | Lijun XU, Hui LI, Zuyang LIU, Kansong CHEN, Weixuan MA. 3D-GA-Unet: MRI image segmentation algorithm for glioma based on 3D-Ghost CNN [J]. Journal of Computer Applications, 2024, 44(4): 1294-1302. |
[12] | Yongfeng DONG, Jiaming BAI, Liqin WANG, Xu WANG. Chinese named entity recognition combining prior knowledge and glyph features [J]. Journal of Computer Applications, 2024, 44(3): 702-708. |
[13] | Jingxian ZHOU, Xina LI. UAV detection and recognition based on improved convolutional neural network and radio frequency fingerprint [J]. Journal of Computer Applications, 2024, 44(3): 876-882. |
[14] | Ruifeng HOU, Pengcheng ZHANG, Liyuan ZHANG, Zhiguo GUI, Yi LIU, Haowen ZHANG, Shubin WANG. Iterative denoising network based on total variation regular term expansion [J]. Journal of Computer Applications, 2024, 44(3): 916-921. |
[15] | Rui ZHANG, Siqi SONG, Jing HU, Yongmei ZHANG, Yanfeng CHAI. Performance evaluation of industry-university-research based on statistics and adaptive ParNet [J]. Journal of Computer Applications, 2024, 44(2): 628-637. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||