计算机科学
人工智能
特征提取
稳健性(进化)
模式识别(心理学)
特征(语言学)
目标检测
计算机视觉
水准点(测量)
大地测量学
语言学
生物化学
基因
哲学
化学
地理
作者
Ruiheng Zhang,Lu Li,Qi Zhang,J.Y. Zhang,Lixin Xu,Baomin Zhang,Binglu Wang
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-1
被引量:44
标识
DOI:10.1109/tcsvt.2023.3289142
摘要
The combination of infrared and visible videos aims to gather more comprehensive feature information from multiple sources and reach superior results on various practical tasks, such as detection and segmentation, over that of a single modality. However, most existing dual-modality object detection algorithms ignore the modal differences and fail to consider the correlation between feature extraction and fusion, which leads to incomplete extraction and inadequate fusion of dual-modality features. Hence, there raises an issue of how to preserve each unique modal feature and fully utilize the complementary infrared and visible information. Facing the above challenges, we propose a novel Differential Feature Awareness Network (DFANet) within antagonistic learning for infrared and visible object detection. The proposed model consists of an Antagonistic Feature Extraction with Divergence (AFED) module used to extract the differential infrared and visible features with unique information, and an Attention-based Differential Feature Fusion (ADFF) module used to fully fuse the extracted differential features. We conduct performance comparisons with existing state-of-the-art models on two benchmark datasets to represent the robustness and superiority of DFANet, and numerous ablation experiments to illustrate its effectiveness.
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