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Composite Object Relation Modeling for Few-Shot Scene Recognition

文献类型：期刊论文

作者Song, Xinhang1,2; Liu, Chenlong1,2; Zeng, Haitao1,2; Zhu, Yaohui1,2; Chen, Gongwei1,2; Qin, Xiaorong1,2; Jiang, Shuqiang1,2
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2023
卷号32页码:5678-5691
关键词Scene recognition few-shot learning graph modeling generalization ability
ISSN号1057-7149
DOI10.1109/TIP.2023.3321475
英文摘要The goal of few-shot image recognition is to classify different categories with only one or a few training samples. Previous works of few-shot learning mainly focus on simple images, such as object or character images. Those works usually use a convolutional neural network (CNN) to learn the global image representations from training tasks, which are then adapted to novel tasks. However, there are many more abstract and complex images in real world, such as scene images, consisting of many object entities with flexible spatial relations among them. In such cases, global features can hardly obtain satisfactory generalization ability due to the large diversity of object relations in the scenes, which may hinder the adaptability to novel scenes. This paper proposes a composite object relation modeling method for few-shot scene recognition, capturing the spatial structural characteristic of scene images to enhance adaptability on novel scenes, considering that objects commonly co- occurred in different scenes. In different few-shot scene recognition tasks, the objects in the same images usually play different roles. Thus we propose a task-aware region selection module (TRSM) to further select the detected regions in different few-shot tasks. In addition to detecting object regions, we mainly focus on exploiting the relations between objects, which are more consistent to the scenes and can be used to cleave apart different scenes. Objects and relations are used to construct a graph in each image, which is then modeled with graph convolutional neural network. The graph modeling is jointly optimized with few-shot recognition, where the loss of few-shot learning is also capable of adjusting graph based representations. Typically, the proposed graph based representations can be plugged in different types of few-shot architectures, such as metric-based and meta-learning methods. Experimental results of few-shot scene recognition show the effectiveness of the proposed method.
资助项目Beijing Natural Science Foundation[Z190020] ; Beijing Natural Science Foundation[JQ22012] ; National Natural Science Foundation of China[62125207] ; National Natural Science Foundation of China[62032022] ; National Natural Science Foundation of China[62272443] ; National Natural Science Foundation of China[U1936203]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001087959700005
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/21087]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Jiang, Shuqiang
作者单位1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Song, Xinhang,Liu, Chenlong,Zeng, Haitao,et al. Composite Object Relation Modeling for Few-Shot Scene Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:5678-5691.
APA Song, Xinhang.,Liu, Chenlong.,Zeng, Haitao.,Zhu, Yaohui.,Chen, Gongwei.,...&Jiang, Shuqiang.(2023).Composite Object Relation Modeling for Few-Shot Scene Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,5678-5691.
MLA Song, Xinhang,et al."Composite Object Relation Modeling for Few-Shot Scene Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):5678-5691.

入库方式： OAI收割

来源：计算技术研究所

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