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• W2930955038 abstract "In most recent years, zero-shot recognition (ZSR) has gained increasing attention in machine learning and image processing fields. It aims at recognizing unseen class instances with knowledge transferred from seen classes. This is typically achieved by exploiting a pre-defined semantic feature space (FS), i.e., semantic attributes or word vectors, as a bridge to transfer knowledge between seen and unseen classes. However, due to the absence of unseen classes during training, the conventional ZSR easily suffers from domain shift and hubness problems. In this paper, we propose a novel ZSR learning framework that can handle these two issues well by adaptively adjusting semantic FS. To the best of our knowledge, our work is the first to consider the adaptive adjustment of semantic FS in ZSR. Moreover, our solution can be formulated to a more efficient framework that significantly boosts the training. Extensive experiments show the remarkable performance improvement of our model compared with other existing methods." @default.
• W2930955038 created "2019-04-11" @default.
• W2930955038 creator A5038505509 @default.
• W2930955038 creator A5043464306 @default.
• W2930955038 date "2019-05-01" @default.
• W2930955038 modified "2023-09-24" @default.
• W2930955038 title "Adaptive Adjustment with Semantic Feature Space for Zero-shot Recognition" @default.
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• W2930955038 doi "https://doi.org/10.1109/icassp.2019.8682869" @default.
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