Towards Natural Image Matting in the Wild via Real-Scenario Prior Publication date: 9 Oct 2024 Topic: Semantic Segmentation Paper: https://arxiv.org/pdf/2410.06593v1.pdf GitHub: https://github.com/xiarho/semat Description: 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.