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This paper presents a novel framework called DDFUS for zero-shot sketch-based image retrieval (ZS-SBIR) that utilizes hyperbolic neural networks to address challenges related to domain gaps and knowledge transfer. The framework incorporates a contrastive cross-modal learning method and a domain disentanglement approach to enhance the representation and generalization capabilities of visual features. Experimental results demonstrate that DDFUS outperforms existing CNN-based models on multiple datasets, indic

Author

85genw

Language

EN