Towards Natural Image Matting in the Wild via Real-Scenario Prior
Recent approaches attempt to adapt powerful interactive segmentation models,
such as SAM, to interactive matting and fine-tune the models based on synthetic
matting datasets. However, models trained on synthetic data fail to generalize
to complex and occlusion scenes. We address this challenge by proposing a new
matting dataset based on the COCO dataset, namely COCO-Matting. Specifically,
the construction of our COCO-Matting includes accessory fusion and
mask-to-matte, which selects real-world complex images from COCO and converts
semantic segmentation masks to matting labels. The built COCO-Matting comprises
an extensive collection of 38,251 human instance-level alpha mattes in complex
natural scenarios. Furthermore, existing SAM-based matting methods extract
intermediate features and masks from a frozen SAM and only train a lightweight
matting decoder by end-to-end matting losses, which do not fully exploit the
potential of the pre-trained SAM. Thus, we propose SEMat which revamps the
network architecture and training objectives. For network architecture, the
proposed feature-aligned transformer learns to extract fine-grained edge and
transparency features. The proposed matte-aligned decoder aims to segment
matting-specific objects and convert coarse masks into high-precision mattes.
For training objectives, the proposed regularization and trimap loss aim to
retain the prior from the pre-trained model and push the matting logits
extracted from the mask decoder to contain trimap-based semantic information.
Extensive experiments across seven diverse datasets demonstrate the superior
performance of our method, proving its efficacy in interactive natural image
matting. We open-source our code, models, and dataset at
https://github.com/XiaRho/SEMat.
670f125e97bbe79a5028fe6c
Ruihao Xia
claimed_verified
2024-10-16T09:44:00.931Z
65896f5bae21a8ff2883ec1a
/avatars/ffacc166fd0d145504773c666b7c8b94.svg
Ruihao Xia
user
XiaRho
670f125e97bbe79a5028fe6d
Yu Liang
670f125e97bbe79a5028fe6e
Peng-Tao Jiang
670f125e97bbe79a5028fe6f
Hao Zhang
670f125e97bbe79a5028fe70
Qianru Sun
670f125e97bbe79a5028fe71
Yang Tang
670f125e97bbe79a5028fe72
Bo Li
670f125e97bbe79a5028fe73
Pan Zhou
Towards Natural Image Matting in the Wild via Real-Scenario Prior
Recent approaches attempt to adapt powerful interactive segmentation models,
such as SAM, to interactive matting and fine-tune the models based on synthetic
matting datasets. However, models trained on synthetic data fail to generalize
to complex and occlusion scenes. We address this challenge by proposing a new
matting dataset based on the COCO dataset, namely COCO-Matting. Specifically,
the construction of our COCO-Matting includes accessory fusion and
mask-to-matte, which selects real-world complex images from COCO and converts
semantic segmentation masks to matting labels. The built COCO-Matting comprises
an extensive collection of 38,251 human instance-level alpha mattes in complex
natural scenarios. Furthermore, existing SAM-based matting methods extract
intermediate features and masks from a frozen SAM and only train a lightweight
matting decoder by end-to-end matting losses, which do not fully exploit the
potential of the pre-trained SAM. Thus, we propose SEMat which revamps the
network architecture and training objectives. For network architecture, the
proposed feature-aligned transformer learns to extract fine-grained edge and
transparency features. The proposed matte-aligned decoder aims to segment
matting-specific objects and convert coarse masks into high-precision mattes.
For training objectives, the proposed regularization and trimap loss aim to
retain the prior from the pre-trained model and push the matting logits
extracted from the mask decoder to contain trimap-based semantic information.
Extensive experiments across seven diverse datasets demonstrate the superior
performance of our method, proving its efficacy in interactive natural image
matting. We open-source our code, models, and dataset at
https://github.com/XiaRho/SEMat.
A new dataset, COCO-Matting, and a model architecture, SEMat, are introduced to enhance the performance of interactive matting in complex natural scenes by leveraging fine-grained features and trimap-based training objectives.
SAM interactive matting COCO dataset COCO-Matting accessory fusion mask-to-matte feature-aligned transformer matte-aligned decoder regularization trimap loss