计算机科学
RGB颜色模型
背景(考古学)
人工智能
编码器
计算机视觉
管道(软件)
滤波器(信号处理)
目标检测
水准点(测量)
保险丝(电气)
模式识别(心理学)
操作系统
生物
电气工程
工程类
古生物学
程序设计语言
地理
大地测量学
作者
Fushuo Huo,Xuegui Zhu,Lei Zhang,Qifeng Liu,Yu Shu
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2022-05-01
卷期号:32 (5): 3111-3124
被引量:40
标识
DOI:10.1109/tcsvt.2021.3102268
摘要
RGB-T salient object detection (SOD) aims at utilizing the complementary cues of RGB and Thermal (T) modalities to detect and segment the common objects. However, on one hand, existing methods simply fuse the features of two modalities without fully considering the characters of RGB and T. On the other hand, the high computational cost of existing methods prevents them from real-world applications (e.g., automatic driving, abnormal detection, person re-ID). To this end, we proposed an efficient encoder-decoder network named Context-guided Stacked Refinement Network (CSRNet). Specifically, we utilize a lightweight backbone and design efficient decoder parts, which greatly reduce the computational cost. To fuse RGB and T modalities, we proposed an efficient Context-guided Cross Modality Fusion (CCMF) module to filter the noise and explore the complementation of two modalities. Besides, Stacked Refinement Network (SRN) progressively refines the features from top to down via the interaction of semantic and spatial information. Extensive experiments show that our method performs favorably against state-of-the-art algorithms on RGB-T SOD task while with small model size (4.6M), few FLOPs (4.2G), and real-time speed (38 fps). Our codes is available at: https://github.com/huofushuo/CSRNet.
科研通智能强力驱动
Strongly Powered by AbleSci AI