Improving generalized zero-shot learning via cluster-based semantic disentangling representation

Yi Gao, Wentao Feng, Rong Xiao, Lihuo He, Zhenan He, Jiancheng Lv, Chenwei Tang

Abstract: Highlights•The proposed CSDR method improves GZSL by alleviating domain shift and semantic gap.•Semantic disentangling module learns fine-grained semantic feature of visual vectors.•Semantic representation module rises intra-class similarity & inter-discernibility.•Visual-semantic embedding module transfers knowledge from seen class to unseen class.•Experiments on four benchmarks prove effectiveness and superiority of CSDR method.