计算机科学
修补
人工智能
变压器
利用
卷积神经网络
图像(数学)
卷积(计算机科学)
模式识别(心理学)
深度学习
机器学习
人工神经网络
计算机安全
量子力学
物理
电压
作者
Yifan Deng,Le Wang,Sanping Zhou,Deyu Meng,Jinjun Wang
出处
期刊:ACM Multimedia
日期:2021-10-17
被引量:7
标识
DOI:10.1145/3474085.3475426
摘要
Fully Convolutional Networks with attention modules have been proven effective for learning-based image inpainting. While many existing approaches could produce visually reasonable results, the generated images often show blurry textures or distorted structures around corrupted areas. The main reason is due to the fact that convolutional neural networks have limited capacity for modeling contextual information with long range dependencies. Although the attention mechanism can alleviate this problem to some extent, existing attention modules tend to emphasize similarities between the corrupted and the uncorrupted regions while ignoring the dependencies from within each of them. Hence, this paper proposes the Contextual Transformer Network (CTN) which not only learns relationships between the corrupted and the uncorrupted regions but also exploits their respective internal closeness. Besides, instead of a fully convolutional network, in our CTN, we stack several transformer blocks to replace convolution layers to better model the long range dependencies. Finally, by dividing the image into patches of different sizes, we propose a multi-scale multi-head attention module to better model the affinity among various image regions. Experiments on several benchmark datasets demonstrate superior performance by our proposed approach.
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