title: MaGGIe: Masked Guided Gradual Human Instance Matting
summary: Human matting is a foundation task in image and video processing, where human
  foreground pixels are extracted from the input. Prior works either improve the
  accuracy by additional guidance or improve the temporal consistency of a single
  instance across frames. We propose a new framework MaGGIe, Masked Guided
  Gradual Human Instance Matting, which predicts alpha mattes progressively for
  each human instances while maintaining the computational cost, precision, and
  consistency. Our method leverages modern architectures, including transformer
  attention and sparse convolution, to output all instance mattes simultaneously
  without exploding memory and latency. Although keeping constant inference costs
  in the multiple-instance scenario, our framework achieves robust and versatile
  performance on our proposed synthesized benchmarks. With the higher quality
  image and video matting benchmarks, the novel multi-instance synthesis approach
  from publicly available sources is introduced to increase the generalization of
  models in real-world scenarios.
paper_authors._id: 662a994f55fe93e3cdc2e47d
paper_authors.name: Chuong Huynh
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-26T07:42:57.693Z
paper_authors._id: 63a4d196cde2b28f82a56bd9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a4d196cde2b28f82a56bd9/iqVFOtDteRMUScFGRcx0L.png
paper_authors.fullname: Chuong Huynh
paper_authors.type: user
paper_authors.user: chuonghm
paper_authors._id: 662a994f55fe93e3cdc2e47e
paper_authors.name: Seoung Wug Oh
paper_authors._id: 662a994f55fe93e3cdc2e47f
paper_authors.name: Abhinav Shrivastava
paper_authors._id: 662a994f55fe93e3cdc2e480
paper_authors.name: Joon-Young Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-04-26T07:42:59.239Z
paper_authors._id: 64b76c7990b38df83381824b
paper_authors.avatarUrl: /avatars/0a5bec2ea480fb3f43c4b24d59c50e81.svg
paper_authors.fullname: Joon-Young Lee
paper_authors.type: user
paper_authors.user: joonyounglee
paper_title: MaGGIe: Masked Guided Gradual Human Instance Matting
paper_summary: Human matting is a foundation task in image and video processing, where human
  foreground pixels are extracted from the input. Prior works either improve the
  accuracy by additional guidance or improve the temporal consistency of a single
  instance across frames. We propose a new framework MaGGIe, Masked Guided
  Gradual Human Instance Matting, which predicts alpha mattes progressively for
  each human instances while maintaining the computational cost, precision, and
  consistency. Our method leverages modern architectures, including transformer
  attention and sparse convolution, to output all instance mattes simultaneously
  without exploding memory and latency. Although keeping constant inference costs
  in the multiple-instance scenario, our framework achieves robust and versatile
  performance on our proposed synthesized benchmarks. With the higher quality
  image and video matting benchmarks, the novel multi-instance synthesis approach
  from publicly available sources is introduced to increase the generalization of
  models in real-world scenarios.
paper_ai_summary: MaGGIe, a new framework for human instance matting, uses transformer attention and sparse convolution to maintain computational efficiency while achieving robust performance across multiple instances.
paper_ai_keywords: transformer attention sparse convolution multi-instance