title: Matting Anything
summary: In this paper, we propose the Matting Anything Model (MAM), an efficient and
  versatile framework for estimating the alpha matte of any instance in an image
  with flexible and interactive visual or linguistic user prompt guidance. MAM
  offers several significant advantages over previous specialized image matting
  networks: (i) MAM is capable of dealing with various types of image matting,
  including semantic, instance, and referring image matting with only a single
  model; (ii) MAM leverages the feature maps from the Segment Anything Model
  (SAM) and adopts a lightweight Mask-to-Matte (M2M) module to predict the alpha
  matte through iterative refinement, which has only 2.7 million trainable
  parameters. (iii) By incorporating SAM, MAM simplifies the user intervention
  required for the interactive use of image matting from the trimap to the box,
  point, or text prompt. We evaluate the performance of MAM on various image
  matting benchmarks, and the experimental results demonstrate that MAM achieves
  comparable performance to the state-of-the-art specialized image matting models
  under different metrics on each benchmark. Overall, MAM shows superior
  generalization ability and can effectively handle various image matting tasks
  with fewer parameters, making it a practical solution for unified image
  matting. Our code and models are open-sourced at
  https://github.com/SHI-Labs/Matting-Anything.
paper_authors._id: 6482aabe85ce4d2973d47af0
paper_authors.name: Jiachen Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-09T16:55:38.134Z
paper_authors._id: 6305b47efca1d8d92b82e0b4
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paper_authors.fullname: Jiachen Li
paper_authors.type: user
paper_authors.user: jiachenl
paper_authors._id: 6482aabe85ce4d2973d47af1
paper_authors.name: Jitesh Jain
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-09T16:55:41.979Z
paper_authors._id: 623dfe96dcda6a715304cbca
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/623dfe96dcda6a715304cbca/B7V_IbQNXAcotOKE_mJQ4.jpeg
paper_authors.fullname: Jitesh Jain
paper_authors.type: user
paper_authors.user: praeclarumjj3
paper_authors._id: 6482aabe85ce4d2973d47af2
paper_authors.name: Humphrey Shi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-12T13:29:38.536Z
paper_authors._id: 61e1188afc27c0f5e3641eb3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/61e1188afc27c0f5e3641eb3/R4JFlWgYomZFvZ9CLSZfD.jpeg
paper_authors.fullname: Humphrey Shi
paper_authors.type: user
paper_authors.user: Humphrey
paper_title: Matting Anything
paper_summary: In this paper, we propose the Matting Anything Model (MAM), an efficient and
  versatile framework for estimating the alpha matte of any instance in an image
  with flexible and interactive visual or linguistic user prompt guidance. MAM
  offers several significant advantages over previous specialized image matting
  networks: (i) MAM is capable of dealing with various types of image matting,
  including semantic, instance, and referring image matting with only a single
  model; (ii) MAM leverages the feature maps from the Segment Anything Model
  (SAM) and adopts a lightweight Mask-to-Matte (M2M) module to predict the alpha
  matte through iterative refinement, which has only 2.7 million trainable
  parameters. (iii) By incorporating SAM, MAM simplifies the user intervention
  required for the interactive use of image matting from the trimap to the box,
  point, or text prompt. We evaluate the performance of MAM on various image
  matting benchmarks, and the experimental results demonstrate that MAM achieves
  comparable performance to the state-of-the-art specialized image matting models
  under different metrics on each benchmark. Overall, MAM shows superior
  generalization ability and can effectively handle various image matting tasks
  with fewer parameters, making it a practical solution for unified image
  matting. Our code and models are open-sourced at
  https://github.com/SHI-Labs/Matting-Anything.
paper_ai_keywords: Matting Anything Model (MAM) alpha matte visual or linguistic user prompt guidance semantic matting instance matting referring image matting Segment Anything Model (SAM) feature maps lightweight Mask-to-Matte (M2M) module iterative refinement trimap box prompt point prompt text prompt image matting benchmarks state-of-the-art specialized image matting models generalization ability