Rethinking Image Inpainting with Attention Feature Fusion

Shuyi Qu, Kaizhu Huang, Qiufeng Wang*, Bin Dong

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review


Recent image inpainting models have archived significant progress through learning from large-scale data. However, restoring images under complicated scenarios (e.g. large masks or complex textures) remains challenging. We argue that the inadequate learning of global structure and local texture could lead to the artifacts and blur of current models. Inspired by feature fusion methods, we utilize Attention Feature Fusion (AFF) to better aggregate the different levels of features within our inpainting model from two perspectives. 1) We insert AFF through skip connections to pass long-distance textures to late semantics; 2) Our modified multi-dilated blocks with AFF residual could fuse features in different receptive fields. Both strategies aim to strengthen the texture and structure aggregation and reduce the inconsistency of semantics during learning. We show quantitatively and qualitatively that our approach outperforms current methods on benchmark datasets.

Original languageEnglish
Title of host publicationNeural Information Processing - 29th International Conference, ICONIP 2022, Proceedings
EditorsMohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031301100
Publication statusPublished - 2023
Event29th International Conference on Neural Information Processing, ICONIP 2022 - Virtual, Online
Duration: 22 Nov 202226 Nov 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13625 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th International Conference on Neural Information Processing, ICONIP 2022
CityVirtual, Online


  • Feature fusion
  • GAN
  • Image inpainting


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