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- W4294791751 abstract "Generalized zero-shot learning (GZSL) aims to classify both seen classes and unseen classes by training seen classes and the semantic information shared by seen and unseen classes. Some methods try to synthesize unseen samples by unseen semantic information; however, the performance of the GZSL is limited as the distribution of synthesized samples and of real samples is misalignment. To tackle this problem, we propose a Contrast and Aggregation Network (CAN). CAN employs a feature and contrast module to align the real and synthesized feature, which maps the real and synthesized visual feature to a new space and aggregates both real and synthesized features to the centroid of real visual features. Since the semantic information may be lost during the generating and mapping stage, we designed a semantic feature alignment module to project the mapped feature into semantic space. We evaluate our proposed CAN on four benchmark datasets to demonstrate that our method is against the state-of-the-art GZSL methods. The source code is available at https://github.com/fesfa/CAN ." @default.
- W4294791751 created "2022-09-06" @default.
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- W4294791751 date "2022-01-01" @default.
- W4294791751 modified "2023-10-12" @default.
- W4294791751 title "Contrast and Aggregation Network for Generalized Zero-shot Learning" @default.
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- W4294791751 doi "https://doi.org/10.1007/978-3-031-15931-2_32" @default.
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