Few-shot object detection via fusing multi-scale and attention mechanism

doi:10.11772/j.issn.1001-9081.2023050699

Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (5): 1437-1444.DOI: 10.11772/j.issn.1001-9081.2023050699

Special Issue: 人工智能； 2023年中国计算机学会人工智能会议(CCFAI 2023)

• 2023 CCF Conference on Artificial Intelligence (CCFAI 2023) • Previous Articles Next Articles

Few-shot object detection via fusing multi-scale and attention mechanism

Hongtian LI¹, Xinhao SHI¹, Weiguo PAN¹(), Cheng XU¹, Bingxin XU¹, Jiazheng YUAN¹^,²

^1.Beijing Key Laboratory of Information Service Engineering （Beijing Union University），Beijing 100101，China
^2.College of Science and Technology，Beijing Open University，Beijing 100081，China

Received:2023-05-08 Revised:2023-06-11 Accepted:2023-06-16 Online:2023-08-01 Published:2024-05-10
Contact: Weiguo PAN
About author:LI Hongtian， born in 1998， M. S. candidate. His research interests include image processing， computer vision.
SHI Xinhao， born in 1999， M. S. candidate. His research interests include reinforcement learning， computer vision.
XU Cheng， born in 1988， Ph. D.， lecturer. His research interests include cognitive computing， vehicles of Internet security.
XU Bingxin， born in 1985， Ph. D.， associate professor. Her research interests include image processing， computer vision.
YUAN Jiazheng， born in 1971， Ph. D.， professor， His research interests include artificial intelligence， visual computing.
Supported by:
Beijing Natural Sciences Foundation(4232026);National Natural Science Foundation of China(62171042);Beijing Key Science and Technology Project(KZ202211417048);High-level Research and Innovation Team Project of Beijing Higher Education Institutions(BPHR20220120);Collaborative Innovation Center(CYX2203);Beijing Union University Research Project(ZK10202202)

融合多尺度和注意力机制的小样本目标检测

李鸿天¹, 史鑫昊¹, 潘卫国¹(), 徐成¹, 徐冰心¹, 袁家政¹^,²

^1.北京市信息服务工程重点实验室（北京联合大学），北京 100101
^2.北京开放大学科技学院，北京 100081

通讯作者: 潘卫国
作者简介:李鸿天（1998—），男，广东肇庆人，硕士研究生，主要研究方向：图像处理、计算机视觉
史鑫昊（1999—），男，山东日照人，硕士研究生，主要研究方向：强化学习、计算机视觉
徐成（1988—），男，内蒙古乌海人，讲师，博士，主要研究方向：认知计算、车联网安全
徐冰心（1985—），女，吉林吉林人，副教授，博士，CCF会员，主要研究方向：图像处理、计算机视觉
袁家政（1971—），男，湖南邵阳人，教授，博士，主要研究方向：人工智能、视觉计算。
第一联系人：潘卫国（1984—），男，河北邯郸人，副教授，博士，主要研究方向：计算机视觉、智能驾驶
基金资助:
北京市自然科学基金资助项目(4232026);国家自然科学基金资助项目(62171042);北京市重点科技项目(KZ202211417048);北京市属高等学校高水平科研创新团队项目(BPHR20220120);北京市朝阳区协同创新中心资助项目(CYX2203);北京联合大学科研项目(ZK10202202)

Abstract

Abstract:

The existing two-stage few-shot object detection methods based on fine-tuning are not sensitive to the features of new classes， which will cause misjudgment of new classes into base classes with high similarity to them， thus affecting the detection performance of the model. To address the above issue， a few-shot object detection algorithm that incorporates multi-scale and attention mechanism was proposed， namely MA-FSOD （Few-Shot Object Detection via fusing Multi-scale and Attention mechanism）. Firstly， grouped convolutions and large convolution kernels were used to extract more class-discriminative features in the backbone network， and Convolutional Block Attention Module （CBAM） was added to achieve adaptive feature augmentation. Then， a modified pyramid network was used to achieve multi-scale feature fusion， which enables Region Proposal Network （RPN） to accurately find Regions of Interest （RoI） and provide more abundant high-quality positive samples from multiple scales to the classification head. Finally， the cosine classification head was used for classification in the fine-tuning stage to reduce the intra-class variance. Compared with the Few-Shot object detection via Contrastive proposal Encoding （FSCE） algorithm on PASCAL-VOC 2007/2012 dataset， the MA-FSOD algorithm improved AP₅₀ for new classes by 5.6 percentage points； and on the more challenging MSCOCO dataset， compared with Meta-Faster-RCNN， the APs corresponding to 10-shot and 30-shot were improved by 0.1 percentage points and 1.6 percentage points， respectively. Experimental results show that MA-FSOD can more effectively alleviate the misclassification problem and achieve higher accuracy in few-shot object detection than some mainstream few-shot object detection algorithms.

Key words: transfer learning, few-shot object detection, attention mechanism, multi-scale feature fusion, cosine similarity

摘要：

现有基于微调的二阶段小样本目标检测方法对新类特征不敏感，易将新类别误判成与它相似度高的基类，影响模型的检测性能。针对上述问题，提出一种融合多尺度和注意力机制的小样本目标检测（MA-FSOD）算法。首先在骨干网络使用分组卷积和大卷积核提取更具类别区分性的特征，并加入卷积注意力模块（CBAM）实现特征的自适应增强；再通过改进的金字塔网络实现多尺度的特征融合，使候选框生成网络（RPN）可以准确找到感兴趣区域（RoI），从多个尺度向分类头提供更丰富的高质量正样本；最后在微调阶段采用余弦分类头进行分类，降低类内方差。在PASCAL-VOC 2007/2012数据集上与基于候选框编码对比损失的小样本目标检测（FSCE）算法相比，MA-FSOD算法对新类的AP₅₀提升了5.6个百分点；在更具挑战性的MSCOCO数据集中，与Meta-Faster-RCNN相比，10-shot和30-shot对应的AP则分别提升了0.1个百分点和1.6个百分点。实验结果表明，相较于一些主流的小样本目标检测算法，MA?FSOD算法能更有效地缓解误分类问题，实现更高精度的小样本目标检测。

关键词: 迁移学习, 小样本目标检测, 注意力机制, 多尺度特征融合, 余弦相似度

CLC Number:

TP391.4

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.

李鸿天, 史鑫昊, 潘卫国, 徐成, 徐冰心, 袁家政. 融合多尺度和注意力机制的小样本目标检测[J]. 《计算机应用》唯一官方网站, 2024, 44(5): 1437-1444.

Figures/Tables 13

Fig. 1 Structure of MA-FSOD

Fig.2 Interlayer structure comparison between ConvNeXt-tiny and ResNet-101

Fig.3 Structure of CBAM

Fig.4 Multi-scale feature fusion module structure

Tab. 1 Comparison of detection performance under various few-shot conditions for PASCAL-VOC dataset

数据集划分	K-shot	AP₅₀
		基于迁移学习范式的方法				基于元学习范式的方法				MA-FSOD
		TFA w/cos^［6］	MPSR^［9］	FSCE^［11］	FSOD-SR^［12］	FSRW^［15］	Meta R-CNN^［16］	QA-FewDet^［17］	Meta-Faster-RCNN^［19］	MA-FSOD
split1	1-shot	39.8	41.4	44.2	50.1	14.8	19.9	42.4	43.0	46.3
	2-shot	36.1	—	43.8	54.4	15.5	25.5	51.9	54.5	52.4
	3-shot	44.7	51.4	51.4	56.2	26.7	35.0	55.7	60.6	61.4
	5-shot	55.7	55.6	61.9	60.0	33.9	45.7	62.6	66.1	64.8
	10-shot	56.0	61.7	63.4	62.4	47.2	51.5	63.4	65.4	65.4
split2	1-shot	23.5	24.3	27.3	29.5	15.7	10.4	25.9	27.7	33.7
	2-shot	26.9	—	29.5	39.9	15.3	19.4	37.8	35.5	34.4
	3-shot	34.1	39.0	43.5	43.5	22.7	29.6	46.6	46.1	45.1
	5-shot	35.1	39.7	44.2	44.6	30.1	34.8	48.9	47.8	47.3
	10-shot	39.1	47.2	50.2	48.1	40.5	45.4	51.1	51.2	50.7
split3	1-shot	30.8	35.4	37.2	43.6	21.3	14.3	35.2	40.6	47.1
	2-shot	34.8	—	41.9	46.6	25.6	18.2	42.9	46.4	54.3
	3-shot	42.8	42.1	47.5	53.4	28.4	27.5	47.8	53.4	56.1
	5-shot	49.5	48.1	54.6	53.4	42.8	41.2	54.8	59.9	61.6
	10-shot	49.8	49.5	58.5	59.5	45.9	48.1	53.5	58.6	61.8
AP₅₀平均值		39.9	35.7	46.6	49.7	28.4	31.1	48.0	50.5	52.2

Tab. 2 Comparison of detection performance under various few-shot conditions for MSCOCO dataset

小样本条件	评估指标	基于迁移学习范式的方法				基于元学习范式的方法				MA-FSOD
小样本条件	评估指标	TFAw/cos^［6］	MPSR^［9］	FSCE^［11］	FSOD-SR^［12］	FSRW^［15］	Meta R-CNN^［16］	QA-FewDet^［17］	Meta-Faster-RCNN^［19］	MA-FSOD
10-shot	AP	10.0	9.8	11.9	11.6	5.6	8.7	11.6	12.7	12.8
	AP₅₀	19.1	17.9	—	21.7	12.3	19.1	23.9	25.7	25.6
	AP₇₅	9.3	9.7	10.5	10.4	4.6	6.6	9.8	10.8	11.2
30-shot	AP	13.7	14.1	16.4	15.2	9.1	12.4	16.5	16.6	18.2
	AP₅₀	24.9	25.4	—	27.5	19.0	25.3	31.9	31.8	34.6
	AP₇₅	13.4	14.2	16.2	14.6	7.6	10.8	15.5	15.8	17.4

Tab. 3 Ablation experiment results of backbone and multi-scale pyramid networks in VOC07-split1

是否采用 ConvNeXt‑tiny	是否采用本文改进的多尺度特征融合模块	仅在基类推理的AP₅₀/%	不同小样本条件下推理的AP₅₀/%					参数量/10⁶	FLOPs
是否采用 ConvNeXt‑tiny	是否采用本文改进的多尺度特征融合模块	仅在基类推理的AP₅₀/%	1-shot	2-shot	3-shot	5-shot	10-shot	参数量/10⁶	FLOPs
否	否	71.4	29.9	33.2	39.3	47.1	48.4	60.08	40.38
否	是	72.4	35.0	36.0	40.9	48.8	50.6	63.62	42.86
是	否	77.6	36.0	50.0	46.5	61.9	63.4	44.88	33.45
是	是	78.5	39.3	50.8	53.3	62.8	65.2	48.42	35.92

Tab. 4 Ablation experiment results with different classification heads in VOC07-split1

分类头类型	不同小样本条件下推理的AP₅₀
分类头类型	1-shot	2-shot	3-shot	5-shot	10-shot
双头分类器	34.0	44.9	51.0	58.5	62.0
共享FC分类头	39.3	50.8	53.3	62.8	65.2
余弦分类头	41.2	49.6	55.4	64.0	64.9

Tab. 5 Ablation experiment results with different values of α in VOC07-split1

$α$	不同小样本条件下推理的AP₅₀/%
$α$	1-shot	2-shot	3-shot	5-shot	10-shot
10	36.5	47.1	54.9	55.3	55.1
20	41.2	49.6	55.4	64.0	64.9
30	39.1	47.5	51.2	60.9	64.8
40	39.4	47.8	53.1	61.7	63.1

Tab. 5 Ablation experiment results with different values of α in VOC07-split1

$α$	不同小样本条件下推理的AP₅₀/%
$α$	1-shot	2-shot	3-shot	5-shot	10-shot
10	36.5	47.1	54.9	55.3	55.1
20	41.2	49.6	55.4	64.0	64.9
30	39.1	47.5	51.2	60.9	64.8
40	39.4	47.8	53.1	61.7	63.1

Tab. 6 Ablation experiment results of CBAM with fine-tuning strategy in VOC07-split1

实验序号	是否加入CBAM		是否冻结参数				仅在基类推理（AP₅₀）	不同小样本条件下推理（AP₅₀）
实验序号	基类训练	新类微调	CBAM	金字塔模块	RPN	RoI提取器	仅在基类推理（AP₅₀）	1-shot	2-shot	3-shot	5-shot	10-shot
1	是	是	否	否	否	否	79.1	36.9	47.4	55.0	63.8	64.9
2	是	是	是	否	否	否		36.3	47.7	54.7	63.9	65.5
3	是	是	是	是	否	否		39.2	48.8	57.5	64.1	65.3
4	是	是	是	是	是	否		43.7	49.4	56.6	64.2	65.5
5	是	是	是	是	是	是		45.2	52.3	60.4	64.4	65.8
6	是	否	否	是	是	是		52.2	48.9	59.2	64.8	64.8
7	否	否	否	是	是	是	78.5	41.2	49.6	55.4	64.0	64.9

Fig.5 Ablation experimental results of different sampling strategies in VOC07-split1

Fig. 6 Comparison of FSCE and MA-FSOD for visualization of attention allocation at res4 layer

Fig. 7 False detection， misdetection and emergence of uncertainty detection for a new class of FSCE vs. correct detection of MA-FSOD

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Few-shot object detection via fusing multi-scale and attention mechanism

融合多尺度和注意力机制的小样本目标检测

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