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Easy-to-use image segmentation library with awesome pre-trained model zoo, supporting wide-range of practical tasks in Semantic Segmentation, Interactive Segmentation, Panoptic Segmentation, Image Matting, 3D Segmentation, etc.
Python implementation of A. Levin D. Lischinski and Y. Weiss. A Closed Form Solution to Natural Image Matting. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2006, New York
A comprehensive list [AIM@IJCAI'21, P3M@MM'21, GFM@IJCV'22, RIM@CVPR'23, P3MNet@IJCV'23] of our research works related to image matting, including papers, codes, datasets, demos, and citations. Note: The repo for [IJCV'23] "Rethinking Portrait Matting with Privacy Preserving" has been moved to: https://github.com/ViTAE-Transformer/P3M-Net