Domain disentanglement and fusion based on hyperbolic neural networks for zero-shot sketch-based image retrieval

Qing Zhang, Jing Zhang, Xiangdong Su, Yonghe Wang, Feilong Bao, Guanglai Gao

Abstract: Highlights•We propose a ZS-SBIR model based on hyperbolic neural networks.•Hyperbolic space is more suitable for feature representation than Euclidean space.•The domain disentanglement network effectively aligning image and sketch features.•The domain fusion network enhances retrieval feature representation capabilities.•The proposed model surpasses the state-of-the-art models.