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首页> 外文期刊>Continental Shelf Research: A Companion Journal to Deep-Sea Research and Progress in Oceanography >Multi-scale generative adversarial inpainting network based on cross-layer attention transfer mechanism
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Multi-scale generative adversarial inpainting network based on cross-layer attention transfer mechanism

机译：基于交叉层注意力机制的多尺度生成对抗网络

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摘要

Deep learning-based methods have recently shown promising results in image inpainting. These methods generate patches with visually plausible image structures and textures, which are semantically coherent with the context of surrounding regions. However, existing methods tend to generate artifacts which are inconsistent with surrounding regions, especially when dealing with complex images. Aiming at the limitations current in deep learning-based methods, this paper proposes a multi-scale generative adversarial network model based on cross-layer attention transfer mechanism. Cross-Layer Attention Transfer Module (CL-ATM) is presented to guide the filling of the corresponding low-level semantic feature map by using the high-level semantic feature map, so as to ensure visual and semantic consistency of inpainting. Meanwhile, a multi-scale generator and the multi-scale discriminators are added into the network structure. Different scales of discriminators have different receptive fields, which enable the generator to produce images with better global consistency and more details. Qualitative and quantitative experiments show that our method has superior performance against state-of-art inpainting models. (C) 2020 Elsevier B.V. All rights reserved.
机译：最近，基于深度学习的方法在图像染色中显示了有希望的结果。这些方法产生具有视觉上可合理的图像结构和纹理的贴片，这些结构与周围区域的背景是语义相干的。然而，现有方法倾向于产生与周围区域不一致的伪影，特别是在处理复杂图像时。针对基于深度学习的方法的局限性，本文提出了一种基于跨层注意传递机制的多尺度生成的对抗网络模型。提出跨层注意传输模块（CL-ATM）以指导通过使用高级语义特征图来指导相应的低级语义特征图的填充，以确保染色的视觉和语义一致性。同时，将多尺度发生器和多尺度鉴别器添加到网络结构中。不同的判别尺度具有不同的接收领域，使得发电机能够产生具有更好的全局一致性和更多细节的图像。定性和定量实验表明，我们的方法具有卓越的性能，防止最先进的批量模型。 （c）2020 Elsevier B.V.保留所有权利。

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