计算机科学
串联(数学)
发电机(电路理论)
人工智能
迭代法
合并(版本控制)
模式识别(心理学)
情态动词
图像融合
卷积神经网络
构造(python库)
融合
理论计算机科学
算法
机器学习
图像(数学)
数学
情报检索
功率(物理)
化学
物理
量子力学
组合数学
高分子化学
程序设计语言
语言学
哲学
作者
Zhishe Wang,Wenyu Shao,Yanlin Chen,Jiawei Xu,Lei Zhang
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2023-08-01
卷期号:33 (8): 3677-3688
被引量:5
标识
DOI:10.1109/tcsvt.2023.3239627
摘要
Recent existing methods generally adopt a simple concatenation or addition strategy to integrate features at the fusion layer, failing to adequately consider the intrinsic characteristics of different modal images and feature interaction of different scales, which may produce a limited fusion performance. Toward this end, we introduce a cross-scale iterative attentional adversarial fusion network, namely CrossFuse. More specifically, in the generator, we design a cross-modal attention integrated module to merge the intrinsic content of different modal images. The parallel spatial-independent and channel-independent pathways are proposed to calculate the attentional weights, which are assigned to measure the activity levels of source images at the same scale. Moreover, we construct a cross-scale iterative decoder framework to interact with different modality features at different scales, which can constantly optimize their activity levels. By this means, the generator learns to integrate their modality characteristics via attentional weights in an iterative manner, and the generated result characterizes competitive infrared radiant intensity and distinct visible detail description. Extensive experiments on three different benchmarks demonstrate that our CrossFuse outperforms other nine state-of-the-art methods in terms of fusion performance, generalization ability and computational efficiency. Our codes will be released at https://github.com/Zhishe-Wang/CrossFuse .
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