MSFFAL: Few-Shot Object Detection via Multi-Scale Feature Fusion and Attentive Learning

doi:10.3390/s23073609

	MSFFAL: Few-Shot Object Detection via Multi-Scale Feature Fusion and Attentive Learning
	Zhang, Tianzhao 1,2; Sun, Ruoxi1,3 ; Wan, Yong 4; Zhang, Fuping 1; Wei, Jianming 1
	2023-04-01
发表期刊	SENSORS (IF:3.4[JCR-2023],3.7[5-Year])
ISSN	1424-8220
EISSN	1424-8220
卷号	23 期号:7
发表状态	已发表
DOI	10.3390/s23073609
摘要	Few-shot object detection (FSOD) is proposed to solve the application problem of traditional detectors in scenarios lacking training samples. The meta-learning methods have attracted the researchers' attention for their excellent generalization performance. They usually select the same class of support features according to the query labels to weight the query features. However, the model cannot possess the ability of active identification only by using the same category support features, and feature selection causes difficulties in the testing process without labels. The single-scale feature of the model also leads to poor performance in small object detection. In addition, the hard samples in the support branch impact the backbone's representation of the support features, thus impacting the feature weighting process. To overcome these problems, we propose a multi-scale feature fusion and attentive learning (MSFFAL) framework for few-shot object detection. We first design the backbone with multi-scale feature fusion and channel attention mechanism to improve the model's detection accuracy on small objects and the representation of hard support samples. Based on this, we propose an attention loss to replace the feature weighting module. The loss allows the model to consistently represent the objects of the same category in the two branches and realizes the active recognition of the model. The model no longer depends on query labels to select features when testing, optimizing the model testing process. The experiments show that MSFFAL outperforms the state-of-the-art (SOTA) by 0.7-7.8% on the Pascal VOC and exhibits 1.61 times the result of the baseline model in MS COCO's small objects detection.
关键词	few-shot object detection few-shot learning attention mechanism multi-scale feature fusion
URL	查看原文
收录类别	SCI ; EI
语种	英语
资助项目	Special Fund for Basic Research on Scientific Instruments of the National Natural Science Foundation of China[51827814] ; Youth Innovation Promotion Association CAS[2021289]
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000969003700001
出版者	MDPI
EI入藏号	20231613943441
EI主题词	Object detection
EI分类号	723.2 Data Processing and Image Processing ; 912.4 Personnel
原始文献类型	Journal article (JA)
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/296030
专题	信息科学与技术学院信息科学与技术学院_硕士生
通讯作者	Zhang, Fuping
作者单位	1.Chinese Acad Sci, Shanghai Adv Res Inst, Shanghai 201210, Peoples R China 2.Univ Chinese Acad Sci, Sch Elect Elect & Commun Engn, Beijing 100049, Peoples R China 3.ShanghaiTech Univ, Sch Informat Sci & Technol, Shanghai 201210, Peoples R China 4.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Wuhan 430071, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Tianzhao,Sun, Ruoxi,Wan, Yong,et al. MSFFAL: Few-Shot Object Detection via Multi-Scale Feature Fusion and Attentive Learning[J]. SENSORS,2023,23(7).
APA	Zhang, Tianzhao,Sun, Ruoxi,Wan, Yong,Zhang, Fuping,&Wei, Jianming.(2023).MSFFAL: Few-Shot Object Detection via Multi-Scale Feature Fusion and Attentive Learning.SENSORS,23(7).
MLA	Zhang, Tianzhao,et al."MSFFAL: Few-Shot Object Detection via Multi-Scale Feature Fusion and Attentive Learning".SENSORS 23.7(2023).