Optical Instruments, Volume. 45, Issue 6, 14(2023)
MSA-Net: few-shot object detection with multi-stage attention mechanism
Figures & Tables(10)
Fig. 1. Learning strategy for a few-shot object detection framework based on meta-learning
Fig. 7. Example of a model trained under MS COCO 10-shot setting on VOC 2007 dataset
Table 1. Few-shot object detection performance on VOC 2007 test set
View tableView in ArticleTable 1. Few-shot object detection performance on VOC 2007 test set
Model Split 1 Split 2 Split 3 Mean 1 2 3 5 10 1 2 3 5 10 1 2 3 5 10 FRCN[ 22 ]9.9 15.6 21.6 28.0 35.6 9.4 13.8 17.4 21.9 29.8 8.1 13.9 19.0 23.9 31.0 19.93 Deformable-DETR[ 25 ]5.6 13.3 21.7 34.2 45.0 10.9 13.0 18.4 27.3 39.4 7.3 16.6 20.8 32.2 41.8 23.17 RepMet[ 25 ]26.1 32.9 34.4 38.6 41.3 17.2 22.1 23.4 28.3 35.8 27.5 31.1 31.5 34.4 37.2 30.79 FSRW[ 20 ]14.8 15.5 26.7 33.9 47.2 15.7 15.3 22.7 30.1 40.5 21.3 25.6 28.4 42.8 45.9 34.08 Meta RCNN [ 11 ]19.9 25.5 35.0 45.7 51.5 10.4 19.4 29.6 34.8 45.4 14.3 18.2 27.5 41.2 48.1 31.10 TFA[ 9 ]25.3 36.4 42.1 47.9 52.8 18.3 27.5 30.9 34.1 39.5 17.9 27.2 34.3 40.8 45.6 34.71 LSTD[ 19 ]8.2 11.0 12.4 29.1 38.5 11.4 13.8 15.0 15.7 31.0 12.6 18.5 25.0 27.3 36.3 20.39 FSDet[ 20 ]24.2 35.3 42.2 49.1 57.4 21.6 24.6 31.9 37.0 45.7 21.2 30.0 37.2 43.8 49.6 36.72 MSA-Net 26.4 41.5 47.6 49.7 58.9 17.8 26.6 35.4 38.1 46.5 21.4 36.1 39.6 45.6 49.9 38.74
Table 2. Few-shot object detection performance on MS COCO test set
View tableView in ArticleTable 2. Few-shot object detection performance on MS COCO test set
Model k=10 k=30 Average AP AP50 AP75 AP AP50 AP75 AP AP50 AP75 Faster RCNN[ 20 ]5.5 10.0 5.5 7.4 13.1 7.4 6.45 11.55 6.45 Meta-YOLO[ 20 ]5.6 12.3 4.6 9.1 19.0 7.6 7.35 15.65 6.1 Meta Det[ 10 ]7.1 14.6 6.1 11.3 21.7 8.1 9.2 18.15 7.1 Meta RCNN[ 11 ]8.7 19.1 6.6 12.4 25.3 10.8 10.55 22.2 8.7 TFA[ 9 ]9.1 17.1 8.8 12.1 22.0 12.0 10.6 19.55 10.4 MPSR[ 16 ]9.8 17.9 9.7 14.1 25.4 14.2 11.95 21.65 11.95 SRR-FSD[ 26 ]11.3 23.0 9.8 14.7 29.3 13.5 13.0 26.15 13.85 FSCE[ 27 ]11.1 — 9.8 15.3 — 14.2 13.2 — 12.0 Deformable-DETR[ 28 ]11.7 19.6 12.1 16.3 27.2 16.7 14.0 23.4 14.4 MSA-Net 14.5 25.1 15.2 16.1 27.6 16.9 15.3 26.35 16.05
Table 3. Effect of improved modules on mAP (AP50)
View tableView in ArticleTable 3. Effect of improved modules on mAP (AP50)
FRCN Decoupled-layer ABD Multi-scale Attention Base Novel 1 2 3 5 10 √ 56.3 9.9 15.6 21.6 28.0 35.6 √ √ 68.6 13.5 22.5 24.7 29.9 40.1 √ √ 74.5 17.2 27.9 33.1 35.7 45.3 √ √ 75.8 15.0 25.8 28.3 32.9 43.9 √ √ √ 74.6 21.5 35.2 44.5 45.9 54.7 √ √ √ 72.9 19.0 33.7 41.3 43.2 50.1 √ √ √ 79.8 23.4 37.1 45.4 48.6 56.3 √ √ √ √ 82.0 26.4 41.5 47.6 49.7 58.9
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Yingwei TANG, Rongfu ZHANG, Ran DING, Jie ZHANG. MSA-Net: few-shot object detection with multi-stage attention mechanism[J]. Optical Instruments, 2023, 45(6): 14
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Received: Feb. 3, 2023
Accepted: --
Published Online: Feb. 29, 2024
The Author Email: ZHANG Rongfu (zrf@usst.edu.cn)
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