Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (3): 745-751.DOI: 10.11772/j.issn.1001-9081.2023030315
Special Issue: 人工智能
• Artificial intelligence • Previous Articles Next Articles
Xinye LI1,2, Yening HOU1(), Yinghui KONG1,2, Zhiqi YAN1
Received:
2023-03-29
Revised:
2023-04-27
Accepted:
2023-05-05
Online:
2023-05-24
Published:
2024-03-10
Contact:
Yening HOU
About author:
LI Xinye, born in 1969, Ph. D., associate professor. Her research interests include computer vision, deep learning, few-shot learning.Supported by:
通讯作者:
侯晔凝
作者简介:
李新叶(1969—),女,河北平山人,副教授,博士,主要研究方向:计算机视觉、深度学习、少样本学习基金资助:
CLC Number:
Xinye LI, Yening HOU, Yinghui KONG, Zhiqi YAN. Few-shot object detection combining feature fusion and enhanced attention[J]. Journal of Computer Applications, 2024, 44(3): 745-751.
李新叶, 侯晔凝, 孔英会, 燕志旗. 结合特征融合与增强注意力的少样本目标检测[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 745-751.
Add to citation manager EndNote|Ris|BibTeX
URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023030315
k | mAP/% | mAP50/% | mAP75/% |
---|---|---|---|
3 | 11.23 | 22.32 | 10.18 |
5 | 11.99 | 23.79 | 10.79 |
7 | 11.62 | 22.68 | 10.65 |
9 | 10.92 | 21.67 | 9.95 |
11 | 10.95 | 21.83 | 9.82 |
Tab. 1 Experimental results under different k values
k | mAP/% | mAP50/% | mAP75/% |
---|---|---|---|
3 | 11.23 | 22.32 | 10.18 |
5 | 11.99 | 23.79 | 10.79 |
7 | 11.62 | 22.68 | 10.65 |
9 | 10.92 | 21.67 | 9.95 |
11 | 10.95 | 21.83 | 9.82 |
方法 | 1-shot | 2-shot | 3-shot | 5-shot | 10-shot | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mAP | mAP50 | mAP75 | mAP | mAP50 | mAP75 | mAP | mAP50 | mAP75 | mAP | mAP50 | mAP75 | mAP | mAP50 | mAP75 | |
FSOD | 4.26 | 9.55 | 3.56 | 4.45 | 9.73 | 3.74 | 6.01 | 13.08 | 5.04 | 7.16 | 16.08 | 6.29 | 11.10 | 20.40 | 10.60 |
FSOD+iAFF | 4.30 | 9.85 | 3.62 | 4.66 | 10.06 | 3.89 | 6.06 | 13.17 | 5.19 | 7.82 | 16.68 | 6.47 | 11.28 | 22.39 | 10.33 |
FSOD+iAFF+特征增强 | 4.46 | 9.95 | 3.66 | 4.97 | 10.79 | 4.25 | 6.57 | 13.70 | 5.53 | 8.03 | 16.86 | 6.89 | 11.40 | 22.71 | 10.40 |
FFA-FSOD | 4.55 | 9.97 | 3.67 | 5.44 | 11.89 | 4.58 | 6.74 | 14.54 | 5.74 | 8.16 | 17.03 | 7.02 | 11.99 | 23.79 | 10.79 |
Tab. 2 Ablation experiment results
方法 | 1-shot | 2-shot | 3-shot | 5-shot | 10-shot | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
mAP | mAP50 | mAP75 | mAP | mAP50 | mAP75 | mAP | mAP50 | mAP75 | mAP | mAP50 | mAP75 | mAP | mAP50 | mAP75 | |
FSOD | 4.26 | 9.55 | 3.56 | 4.45 | 9.73 | 3.74 | 6.01 | 13.08 | 5.04 | 7.16 | 16.08 | 6.29 | 11.10 | 20.40 | 10.60 |
FSOD+iAFF | 4.30 | 9.85 | 3.62 | 4.66 | 10.06 | 3.89 | 6.06 | 13.17 | 5.19 | 7.82 | 16.68 | 6.47 | 11.28 | 22.39 | 10.33 |
FSOD+iAFF+特征增强 | 4.46 | 9.95 | 3.66 | 4.97 | 10.79 | 4.25 | 6.57 | 13.70 | 5.53 | 8.03 | 16.86 | 6.89 | 11.40 | 22.71 | 10.40 |
FFA-FSOD | 4.55 | 9.97 | 3.67 | 5.44 | 11.89 | 4.58 | 6.74 | 14.54 | 5.74 | 8.16 | 17.03 | 7.02 | 11.99 | 23.79 | 10.79 |
方法 | 主干网络 | mAP | mAP50 | mAP75 |
---|---|---|---|---|
Meta-Det[ | VGG16 | 7.10 | 14.60 | 6.10 |
Meta R-CNN[ | ResNet-101 | 8.70 | 19.10 | 6.60 |
MPSR[ | ResNet-101 | 9.80 | 17.90 | 9.70 |
THR[ | — | 9.80 | 17.90 | 9.70 |
DMNET[ | ResNet-101 | 10.00 | 17.40 | 10.40 |
TFA[ | ResNet-101 | 10.00 | — | 9.30 |
Retentive R-CNN[ | ResNet-101 | 10.50 | — | — |
FSOD-UP[ | ResNet-101 | 11.00 | — | 10.70 |
FSCE[ | ResNet-101 | 11.10 | — | 9.80 |
SRR-FSD[ | ResNet-101 | 11.30 | 23.00 | 9.80 |
QA-FewDet[ | ResNet-101 | 11.60 | 23.90 | 9.80 |
CGDP+FSCN[ | ResNet-50 | 11.30 | 20.30 | — |
FSOD[ | ResNet-50 | 11.10 | 20.40 | 10.60 |
FFA-FSOD | ResNet-50 | 11.99 | 23.79 | 10.79 |
Tab. 3 Comparison of detection performance among various methods on MS COCO dataset under 2-way 10-shot conditions
方法 | 主干网络 | mAP | mAP50 | mAP75 |
---|---|---|---|---|
Meta-Det[ | VGG16 | 7.10 | 14.60 | 6.10 |
Meta R-CNN[ | ResNet-101 | 8.70 | 19.10 | 6.60 |
MPSR[ | ResNet-101 | 9.80 | 17.90 | 9.70 |
THR[ | — | 9.80 | 17.90 | 9.70 |
DMNET[ | ResNet-101 | 10.00 | 17.40 | 10.40 |
TFA[ | ResNet-101 | 10.00 | — | 9.30 |
Retentive R-CNN[ | ResNet-101 | 10.50 | — | — |
FSOD-UP[ | ResNet-101 | 11.00 | — | 10.70 |
FSCE[ | ResNet-101 | 11.10 | — | 9.80 |
SRR-FSD[ | ResNet-101 | 11.30 | 23.00 | 9.80 |
QA-FewDet[ | ResNet-101 | 11.60 | 23.90 | 9.80 |
CGDP+FSCN[ | ResNet-50 | 11.30 | 20.30 | — |
FSOD[ | ResNet-50 | 11.10 | 20.40 | 10.60 |
FFA-FSOD | ResNet-50 | 11.99 | 23.79 | 10.79 |
类别 划分 | K | 不同方法的mAP50/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
THR | Meta-Det | Meta R-CNN | MPSR | TFA | FSOD-UP | FSCE | CGDP+FSCN | FFA-FSOD | ||
Set 1 | 1 | 16.2 | 18.9 | 19.9 | 41.7 | 39.8 | 43.8 | 32.9 | 40.7 | 43.9 |
2 | 19.0 | 20.6 | 25.5 | — | 36.1 | 47.8 | 44.0 | 45.1 | 48.1 | |
3 | 29.4 | 30.2 | 35.0 | 51.4 | 44.7 | 50.3 | 46.8 | 46.5 | 50.3 | |
5 | 37.7 | 36.8 | 45.7 | 55.2 | 55.7 | 55.4 | 52.9 | 57.4 | 52.7 | |
10 | 49.9 | 49.6 | 51.5 | 61.8 | 56.0 | 61.7 | 59.7 | 62.4 | 58.4 | |
Set 2 | 1 | 17.2 | 21.8 | 10.4 | 24.4 | 23.5 | 31.2 | 23.7 | 27.3 | 31.8 |
2 | 18.9 | 23.1 | 19.4 | — | 26.9 | 30.5 | 30.6 | 31.4 | 34.5 | |
3 | 27.8 | 27.8 | 29.6 | 39.2 | 34.1 | 41.2 | 38.4 | 40.8 | 39.7 | |
5 | 36.8 | 31.7 | 34.8 | 39.9 | 35.1 | 42.2 | 43.0 | 42.7 | 43.3 | |
10 | 47.5 | 43.0 | 45.4 | 47.8 | 39.1 | 48.3 | 48.5 | 46.3 | 48.7 | |
Set 3 | 1 | 20.0 | 20.6 | 14.3 | 35.6 | 30.8 | 35.5 | 22.6 | 31.2 | 39.8 |
2 | 23.5 | 23.9 | 18.2 | — | 34.8 | 39.7 | 33.4 | 36.4 | 42.4 | |
3 | 28.9 | 29.4 | 27.5 | 42.3 | 42.8 | 43.9 | 39.5 | 43.7 | 45.7 | |
5 | 44.2 | 43.9 | 41.2 | 48.0 | 49.5 | 50.6 | 47.3 | 50.1 | 48.9 | |
10 | 49.2 | 44.1 | 48.1 | 49.7 | 49.8 | 53.5 | 54.0 | 55.6 | 51.5 |
Tab. 4 Comparison of detection performance among various methods on PASCAL VOC dataset
类别 划分 | K | 不同方法的mAP50/% | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
THR | Meta-Det | Meta R-CNN | MPSR | TFA | FSOD-UP | FSCE | CGDP+FSCN | FFA-FSOD | ||
Set 1 | 1 | 16.2 | 18.9 | 19.9 | 41.7 | 39.8 | 43.8 | 32.9 | 40.7 | 43.9 |
2 | 19.0 | 20.6 | 25.5 | — | 36.1 | 47.8 | 44.0 | 45.1 | 48.1 | |
3 | 29.4 | 30.2 | 35.0 | 51.4 | 44.7 | 50.3 | 46.8 | 46.5 | 50.3 | |
5 | 37.7 | 36.8 | 45.7 | 55.2 | 55.7 | 55.4 | 52.9 | 57.4 | 52.7 | |
10 | 49.9 | 49.6 | 51.5 | 61.8 | 56.0 | 61.7 | 59.7 | 62.4 | 58.4 | |
Set 2 | 1 | 17.2 | 21.8 | 10.4 | 24.4 | 23.5 | 31.2 | 23.7 | 27.3 | 31.8 |
2 | 18.9 | 23.1 | 19.4 | — | 26.9 | 30.5 | 30.6 | 31.4 | 34.5 | |
3 | 27.8 | 27.8 | 29.6 | 39.2 | 34.1 | 41.2 | 38.4 | 40.8 | 39.7 | |
5 | 36.8 | 31.7 | 34.8 | 39.9 | 35.1 | 42.2 | 43.0 | 42.7 | 43.3 | |
10 | 47.5 | 43.0 | 45.4 | 47.8 | 39.1 | 48.3 | 48.5 | 46.3 | 48.7 | |
Set 3 | 1 | 20.0 | 20.6 | 14.3 | 35.6 | 30.8 | 35.5 | 22.6 | 31.2 | 39.8 |
2 | 23.5 | 23.9 | 18.2 | — | 34.8 | 39.7 | 33.4 | 36.4 | 42.4 | |
3 | 28.9 | 29.4 | 27.5 | 42.3 | 42.8 | 43.9 | 39.5 | 43.7 | 45.7 | |
5 | 44.2 | 43.9 | 41.2 | 48.0 | 49.5 | 50.6 | 47.3 | 50.1 | 48.9 | |
10 | 49.2 | 44.1 | 48.1 | 49.7 | 49.8 | 53.5 | 54.0 | 55.6 | 51.5 |
1 | 曹家乐,李亚利,孙汉卿,等.基于深度学习的视觉目标检测技术综述[J].中国图象图形学报,2022,27(6):1697-1722. 10.11834/jig.220069 |
CAO J L, LI Y L, SUN H Q, et al. A survey on deep learning based visual object detection [J]. Journal of Image and Graphics, 2022,27(6):1697-1722. 10.11834/jig.220069 | |
2 | 赵永强, 饶元, 董世鹏, 等. 深度学习目标检测方法综述[J]. 中国图象图形学报, 2020, 25(4): 629-654. 10.11834/jig.190307 |
ZHAO Y Q, RAO Y, DONG S P, et al. Survey on deep learning object detection [J]. Journal of Image and Graphics, 2020, 25(4): 629-654. 10.11834/jig.190307 | |
3 | 张振伟,郝建国,黄健,等. 小样本图像目标检测研究综述[J]. 计算机工程与应用, 2022,58(5):1-11. 10.3778/j.issn.1002-8331.2109-0405 |
ZHANG Z W, HAO J G, HUANG J, et al. Review of few-shot object detection [J]. Computer Engineering and Applications, 2022, 58(5): 1-11. 10.3778/j.issn.1002-8331.2109-0405 | |
4 | WANG X, HUANG T E, DARRELL T, et al. Frustratingly simple few-shot object detection [C]// Proceedings of the 37th International Conference on Machine Learning. New York: JMLR.org, 2020: 9919-9928. |
5 | ZHANG W, WANG Y-X. Hallucination improves few-shot object detection [C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 13003-13012. 10.1109/cvpr46437.2021.01281 |
6 | ZHANG G, LUO Z, CUI K, et al. Meta-DETR: few-shot object detection via unified image-level meta-learning [EB/OL]. [2022-05-22]. . 10.1109/tpami.2022.3195735 |
7 | FAN Z, MA Y, LI Z, et al. Generalized few-shot object detection without forgetting [C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 4525-4534. 10.1109/cvpr46437.2021.00450 |
8 | HU H, BAI S, LI A, et al. Dense relation distillation with context-aware aggregation for few-shot object detection [C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 10180-10189. 10.1109/cvpr46437.2021.01005 |
9 | XIAO Y, LEPETIT V, MARLET R. Few-shot object detection and viewpoint estimation for objects in the wild [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(3): 3090-3106. |
10 | FAN Q, ZHUO W, TANG C-K, et al. Few-shot object detection with Attention-RPN and multi-relation detector [C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 4012-4021. 10.1109/cvpr42600.2020.00407 |
11 | LENG J, CHEN T, GAO X, et al. A comparative review of recent few-shot object detection algorithms [EB/OL]. [2023-01-22]. . |
12 | DAI Y, GIESEKE F, OEHMCKE S, et al. Attentional feature fusion [C]// Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2021: 3559-3568. 10.1109/wacv48630.2021.00360 |
13 | WANG Y-X, RAMANAN D, HEBERT M. Meta-learning to detect rare objects [C]// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 9924-9933. 10.1109/iccv.2019.01002 |
14 | YAN X, CHEN Z, XU A, et al. Meta R-CNN: towards general solver for instance-level low-shot learning [C]// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 9576-9585. 10.1109/iccv.2019.00967 |
15 | WU J, LIU S, HUANG D, et al. Multi-scale positive sample refinement for few-shot object detection [C]// Proceedings of the 2020 European Conference on Computer Vision. Cham: Springer, 2020: 456-472. 10.1007/978-3-030-58517-4_27 |
16 | ZHANG D, PU H, LI F, et al. Few-shot object detection based on the Transformer and high-resolution network [J]. Computers, Materials & Continua, 2023, 74(2): 3439-3454. 10.32604/cmc.2023.027267 |
17 | LU Y, CHEN X, WU Z, et al. Decoupled metric network for single-stage few-shot object detection [J]. IEEE Transactions on Cybernetics, 2023, 53(1): 514-525. 10.1109/tcyb.2022.3149825 |
18 | WU A, HAN Y, ZHU L, et al. Universal-prototype enhancing for few-shot object detection [C]// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 9547-9556. 10.1109/iccv48922.2021.00943 |
19 | SUN B, LI B, CAI S, et al. FSCE: few-shot object detection via contrastive proposal encoding [C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 7348-7358. 10.1109/cvpr46437.2021.00727 |
20 | ZHU C, CHEN F, AHMED U, et al. Semantic relation reasoning for shot-stable few-shot object detection [C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 8778-8787. 10.1109/cvpr46437.2021.00867 |
21 | HAN G, HE Y, HUANG S, et al. Query adaptive few-shot object detection with heterogeneous graph convolutional networks [C]// Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2021: 3243-3252. 10.1109/iccv48922.2021.00325 |
22 | LI Y, ZHU H, CHENG Y, et al. Few-shot object detection via classification refinement and distractor retreatment [C]// Proceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2021: 15390-15398. 10.1109/cvpr46437.2021.01514 |
[1] | Yexin PAN, Zhe YANG. Optimization model for small object detection based on multi-level feature bidirectional fusion [J]. Journal of Computer Applications, 2024, 44(9): 2871-2877. |
[2] | Yeheng LI, Guangsheng LUO, Qianmin SU. Logo detection algorithm based on improved YOLOv5 [J]. Journal of Computer Applications, 2024, 44(8): 2580-2587. |
[3] | Yingjun ZHANG, Niuniu LI, Binhong XIE, Rui ZHANG, Wangdong LU. Semi-supervised object detection framework guided by curriculum learning [J]. Journal of Computer Applications, 2024, 44(8): 2326-2333. |
[4] | Song XU, Wenbo ZHANG, Yifan WANG. Lightweight video salient object detection network based on spatiotemporal information [J]. Journal of Computer Applications, 2024, 44(7): 2192-2199. |
[5] | Ruihua LIU, Zihe HAO, Yangyang ZOU. Gait recognition algorithm based on multi-layer refined feature fusion [J]. Journal of Computer Applications, 2024, 44(7): 2250-2257. |
[6] | Xun SUN, Ruifeng FENG, Yanru CHEN. Monocular 3D object detection method integrating depth and instance segmentation [J]. Journal of Computer Applications, 2024, 44(7): 2208-2215. |
[7] | 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. |
[8] | Yue LIU, Fang LIU, Aoyun WU, Qiuyue CHAI, Tianxiao WANG. 3D object detection network based on self-attention mechanism and graph convolution [J]. Journal of Computer Applications, 2024, 44(6): 1972-1977. |
[9] | Yaping DENG, Yingjiang LI. Review of YOLO algorithm and its applications to object detection in autonomous driving scenes [J]. Journal of Computer Applications, 2024, 44(6): 1949-1958. |
[10] | Xinyan YU, Cheng ZENG, Qian WANG, Peng HE, Xiaoyu DING. Few-shot news topic classification method based on knowledge enhancement and prompt learning [J]. Journal of Computer Applications, 2024, 44(6): 1767-1774. |
[11] | Hongtian LI, Xinhao SHI, Weiguo PAN, Cheng XU, Bingxin XU, Jiazheng YUAN. Few-shot object detection via fusing multi-scale and attention mechanism [J]. Journal of Computer Applications, 2024, 44(5): 1437-1444. |
[12] | Huantong GENG, Zhenyu LIU, Jun JIANG, Zichen FAN, Jiaxing LI. Embedded road crack detection algorithm based on improved YOLOv8 [J]. Journal of Computer Applications, 2024, 44(5): 1613-1618. |
[13] | Xiaogang SONG, Dongdong ZHANG, Pengfei ZHANG, Li LIANG, Xinhong HEI. Real-time object detection algorithm for complex construction environments [J]. Journal of Computer Applications, 2024, 44(5): 1605-1612. |
[14] | Zhihao WU, Ziqiu CHI, Ting XIAO, Zhe WANG. Meta-learning adaption for few-shot text-to-speech [J]. Journal of Computer Applications, 2024, 44(5): 1629-1635. |
[15] | Xin LI, Qiao MENG, Junyi HUANGFU, Lingchen MENG. YOLOv5 multi-attribute classification based on separable label collaborative learning [J]. Journal of Computer Applications, 2024, 44(5): 1619-1628. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||