Semantics-Adding Flaw-Erasing Network for Semantic Human Matting

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationThe 33rd British Machine Vision Conference Proceedings
PublisherBMVA Press
Number of pages13
Publication statusPublished - Nov 2022

Publication series

NameBMVC 2022 - 33rd British Machine Vision Conference Proceedings

Conference

Title33rd British Machine Vision Conference (BMVC 2022)
PlaceUnited Kingdom
CityLondon
Period21 - 24 November 2022

Abstract

Addressing human image matting without trimap is very challenging. The latest methods rely on estimating a segmentation map or a pseudo trimap to constrain the matting process. However, their matting accuracy typically affects by the errors in these auxiliary maps. Motivated by recent flaw correction approaches, we propose a novel neural approach to address this problem: We first train a model to directly compute an initial matte, of which the errors are further detected by a flaw detector and corrected by a refinement process. Our method, named Semantics-Adding Flaw-Erasing network (SAFE-Net), has two novel modules: a Semantic Addition module (SAM) to enrich matting features with human semantics via an attention mechanism and a Flaw Elimination module (FEM) to correct errors in the defective matte regions. To facilitate the learning process, we have further constructed a large human matting dataset containing 4,729 unique foregrounds with fine annotations. Extensive experiments demonstrate that SAFE-Net outperforms existing trimap-free human image matting methods.

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Citation Format(s)

Semantics-Adding Flaw-Erasing Network for Semantic Human Matting. / Sun, Jiayu; Ke, Zhanghan; Xu, Ke et al.
The 33rd British Machine Vision Conference Proceedings. BMVA Press, 2022. (BMVC 2022 - 33rd British Machine Vision Conference Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review