期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 25, 期 -, 页码 1649-1664出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2022.3145666
关键词
Bidirectional network; generative model; zero-shot learning
Zero-shot learning aims to recognize unknown categories that are not available during training. Generative models have shown potential to address this problem by synthesizing unseen features based on semantic embeddings. We propose a visual-semantic aligned bidirectional network with cycle consistency to bridge the gap between visual and semantic spaces and generate high-quality unseen features. Two carefully designed strategies are incorporated to improve the overall ZSL performance by enhancing intra-domain class divergence and mitigating inter-domain shift.
Zero-shot learning (ZSL) aims to recognize unknown categories that are unavailable during training. Recently, generative models have shown the potential to address this challenging problem by synthesizing unseen features conditioned on semantic embeddings such as attributes. However, unidirectional generative models cannot guarantee the effective coupling between visual and semantic spaces. To this end, we propose a visual-semantic aligned bidirectional network with cycle consistency to alleviate the gap between these two spaces, generating unseen features of high quality. More importantly, we incorporate two carefully designed strategies into our bidirectional framework to improve the overall ZSL performance. Specifically, we enhance the intra-domain class divergence in both visual and semantic spaces, and in the meantime, mitigate the inter-domain shift to preserve seen-unseen domain discrimination. Experimental results on four standard benchmarks show the superiority of our framework over existing state-of-the-art methods under both conventional and generalized ZSL settings.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据