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TransWeaver: Weave Image Pairs for Class Agnostic Common Object Detection

文献类型：期刊论文

作者Guo, Xiaoqian1,3; Li, Xiangyang1,3; Wang, Yaowei2; Jiang, Shuqiang1,2,3
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2023
卷号32页码:2947-2959
关键词Proposals Object detection Task analysis Feature extraction Visualization Training Measurement Common object detection transweaver transformer
ISSN号1057-7149
DOI10.1109/TIP.2023.3275870
英文摘要Measuring the similarity of two images is of crucial importance in computer vision. Class agnostic common object detection is a nascent research topic about mining image similarity, which aims to detect common object pairs from two images without category information. This task is general and less restrictive which explores the similarity between objects and can further describe the commonality of image pairs at the object level. However, previous works suffer from features with low discrimination caused by the lack of category information. Moreover, most existing methods compare objects extracted from two images in a simple and direct way, ignoring the internal relationships between objects in the two images. To overcome these limitations, in this paper, we propose a new framework called TransWeaver, which learns intrinsic relationships between objects. Our TransWeaver takes image pairs as input and flexibly captures the inherent correlation between candidate objects from two images. It consists of two modules (i.e., the representation-encoder and the weave-decoder) and captures efficient context information by weaving image pairs to make them interact with each other. The representation-encoder is used for representation learning, which can obtain more discriminative representations for candidate proposals. Furthermore, the weave-decoder weaves the objects from two images and is able to explore the inter-image and intra-image context information at the same time, bringing a better object matching ability. We reorganize the PASCAL VOC, COCO, and Visual Genome datasets to obtain training and testing image pairs. Extensive experiments demonstrate the effectiveness of the proposed TransWeaver which achieves state-of-the-art performance on all datasets.
资助项目National Natural Science Foundation of China[62125207] ; National Natural Science Foundation of China[62102400] ; National Natural Science Foundation of China[U19B2040] ; National Natural Science Foundation of China[U20B2052] ; National Postdoctoral Program for Innovative Talents[BX20200338] ; Beijing Natural Science Foundation[Z190020]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000995885700006
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/21207]  
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.Peng Cheng Lab, Shenzhen 518055, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Xiaoqian,Li, Xiangyang,Wang, Yaowei,et al. TransWeaver: Weave Image Pairs for Class Agnostic Common Object Detection[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2023,32:2947-2959.
APA Guo, Xiaoqian,Li, Xiangyang,Wang, Yaowei,&Jiang, Shuqiang.(2023).TransWeaver: Weave Image Pairs for Class Agnostic Common Object Detection.IEEE TRANSACTIONS ON IMAGE PROCESSING,32,2947-2959.
MLA Guo, Xiaoqian,et al."TransWeaver: Weave Image Pairs for Class Agnostic Common Object Detection".IEEE TRANSACTIONS ON IMAGE PROCESSING 32(2023):2947-2959.

入库方式： OAI收割

来源：计算技术研究所

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