Matting Anything
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.
6482aabe85ce4d2973d47af0
Jiachen Li
claimed_verified
2023-06-09T16:55:38.134Z
6305b47efca1d8d92b82e0b4
https://cdn-avatars.huggingface.co/v1/production/uploads/6305b47efca1d8d92b82e0b4/6G1QlXHSeiSx9rPnUvjhz.jpeg
Jiachen Li
user
jiachenl
6482aabe85ce4d2973d47af1
Jitesh Jain
claimed_verified
2023-06-09T16:55:41.979Z
623dfe96dcda6a715304cbca
https://cdn-avatars.huggingface.co/v1/production/uploads/623dfe96dcda6a715304cbca/B7V_IbQNXAcotOKE_mJQ4.jpeg
Jitesh Jain
user
praeclarumjj3
6482aabe85ce4d2973d47af2
Humphrey Shi
admin_assigned
2023-06-12T13:29:38.536Z
61e1188afc27c0f5e3641eb3
https://cdn-avatars.huggingface.co/v1/production/uploads/61e1188afc27c0f5e3641eb3/R4JFlWgYomZFvZ9CLSZfD.jpeg
Humphrey Shi
user
Humphrey
Matting Anything
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.
The Matting Anything Model (MAM) is a single, efficient image matting framework that leverages SAM and a lightweight M2M module for flexible user guidance and performs comparably to specialized models with fewer parameters.
Matting Anything Model MAM alpha matte image matting semantic matting instance matting referring image matting Segment Anything Model SAM Mask-to-Matte M2M module iterative refinement