Paper: https://arxiv.org/pdf/2401.03407)
Github: https://github.com/ZhengPeng7/BiRefNet)
This research introduces BiRefNet, a novel deep learning framework for high-resolution dichotomous image segmentation. BiRefNet uses a bilateral reference mechanism, incorporating both original image patches and gradient maps, to improve the accuracy of segmenting fine details. The framework is composed of localization and reconstruction modules, enhancing performance through multi-stage supervision and other training strategies. Extensive experiments demonstrate BiRefNet's superior performance across several image segmentation tasks, outperforming existing state-of-the-art methods. The authors also highlight the model's potential applications and its adoption by the community for various third-party projects.
ai , computer vision , cv , nankai university , artificial intelligence , arxiv , research , paper , publication , lvm , large visual models, llm
