中文版 | English
Title

Does Thermal Really Always Matter for RGB-T Salient Object Detection?

Author
Publication Years
2022
DOI
Source Title
ISSN
1520-9210
EISSN
1941-0077
VolumePPIssue:99Pages:1-12
Abstract
In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGB-T SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://rmcong.github.io/proj_TNet.html.
Keywords
URL[Source Record]
Language
English
SUSTech Authorship
Others
ESI Research Field
COMPUTER SCIENCE
Scopus EID
2-s2.0-85140716697
Data Source
Scopus
PDF urlhttps://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9926193
Citation statistics
Cited Times [WOS]:0
Document TypeJournal Article
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/407151
DepartmentDepartment of Computer Science and Engineering
Affiliation
1.Institute of Information Science, Beijing Jiaotong University, Beijing, China
2.Department of Computer Science and Technology, Southern University of Science and Technology, Shenzhen, China
3.School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China
4.Department of Computer Science, City University of Hong Kong, Hong Kong SAR, China
Recommended Citation
GB/T 7714
Cong,Runmin,Zhang,Kepu,Zhang,Chen,et al. Does Thermal Really Always Matter for RGB-T Salient Object Detection?[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2022,PP(99):1-12.
APA
Cong,Runmin.,Zhang,Kepu.,Zhang,Chen.,Zheng,Feng.,Zhao,Yao.,...&Kwong,Sam.(2022).Does Thermal Really Always Matter for RGB-T Salient Object Detection?.IEEE TRANSACTIONS ON MULTIMEDIA,PP(99),1-12.
MLA
Cong,Runmin,et al."Does Thermal Really Always Matter for RGB-T Salient Object Detection?".IEEE TRANSACTIONS ON MULTIMEDIA PP.99(2022):1-12.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Cong,Runmin]'s Articles
[Zhang,Kepu]'s Articles
[Zhang,Chen]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[Cong,Runmin]'s Articles
[Zhang,Kepu]'s Articles
[Zhang,Chen]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Cong,Runmin]'s Articles
[Zhang,Kepu]'s Articles
[Zhang,Chen]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.