ZS-VAT:学习无偏向的属性知识,通过视觉属性转换器实现零射击识别
IEEE transactions on neural networks and learning systems
|April 23, 2024
概括
本研究引入了一种新的视觉属性转换器,用于零射击学习 (ZSL),以解决有偏见的属性知识. ZS-VAT模型有效地学习了无偏的属性知识,提高了零射击识别性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 零射击学习 (ZSL) 依赖于属性知识来进行知识传递.
- 现有的ZSL方法通常学习有偏见的属性知识,阻碍识别性能.
研究的目的:
- 提出一种新的视觉属性变压器,用于零射击识别 (ZS-VAT).
- 学习无偏见的属性知识,提高ZSL的性能.
主要方法:
- 开发了一种属性头自我注意力 (AHSA) 机制,以学习无偏的属性知识.
- 引入了属性融合模型 (AFM) 来从属性知识中恢复类别知识.
- 结合属性嵌入预测 (AEP) 和全球嵌入预测 (GEP) 进行最终的语义预测.
主要成果:
- ZS-VAT有效地学习了无偏的属性知识,减少了属性之间的相互影响.
- AHSA和AFM展示了协同增强知识的方法.
- 拟议的方案在两种通用ZSL (GZSL) 的基准数据集上取得了最先进的结果.
结论:
- 在ZSL中,ZS-VAT提供了一种有效和可解释的解决方案,用于学习ZSL中不偏见的属性知识.
- 与现有的GZSL方法相比,拟议的方法显著提高了零射击识别性能.
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