Free-Form Image Inpainting via Contrastive Attention Network | |
Xin,Ma1,2,3; Xiaoqiang Zhou2,3,4; Huaibo Huang1,3; Zhenhua Chai2; Xiaolin Wei2; Ran He1,3 | |
2021-01 | |
会议名称 | International Conference on Pattern Recognition (ICPR) |
会议日期 | Jan 10-15, 2021 |
会议地点 | Milan, Italy |
出版者 | IEEE |
摘要 | Most deep learning based image inpainting approaches adopt autoencoder or its variants to fill missing regions in images. Encoders are usually utilized to learn powerful representational spaces, which are important for dealing with sophisticated learning tasks. Specifically, in image inpainting tasks, masks with any shapes can appear anywhere in images (i.e., free-form masks) which form complex patterns. It is difficult for encoders to capture such powerful representations under this complex situation. To tackle this problem, we propose a self-supervised Siamese inference network to improve the robustness and generalization. It can encode contextual semantics from full resolution images and obtain more discriminative representations. we further propose a multi-scale decoder with a novel dual attention fusion module (DAF), which can combine both the restored and known regions in a smooth way. This multi-scale architecture is beneficial for decoding discriminative representations learned by encoders into images layer by layer. In this way, unknown regions will be filled naturally from outside to inside. Qualitative and quantitative experiments on multiple datasets, including facial and natural datasets (i.e., Celeb-HQ, Pairs Street View, Places2 and ImageNet), demonstrate that our proposed method outperforms state-of-the-art methods in generating high-quality inpainting results. |
收录类别 | EI |
七大方向——子方向分类 | 图像视频处理与分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44842 |
专题 | 智能感知与计算研究中心 |
通讯作者 | Ran He |
作者单位 | 1.School of Artificial Intelligence,University of Chinese Academy of Sciences 2.Vision Intelligence Center, AI Platform, Meituandianping Group 3.NLPR & CEBSIT, CASIA 4.University of Science and Technology of China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Xin,Ma,Xiaoqiang Zhou,Huaibo Huang,et al. Free-Form Image Inpainting via Contrastive Attention Network[C]:IEEE,2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Free-Form Image Inpa(2477KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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