反事实样本合成和训练强大的视觉问题答案
概括
这项研究引入了一种新方法,使视觉问题答案 (VQA) 模型不那么偏向于语言. 反事实样本合成和培训 (CSST) 战略提高了模型的可解释性和对细节质疑的敏感性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 当前的视觉问题答案 (VQA) 模型往往依赖于肤浅的语言模式,导致分布外数据集的概括性差.
- 减少语言偏差的现有方法,如辅助单问模型,是复杂的,不能保证视觉解释性或问题敏感性.
研究的目的:
- 提出一种名为CSST (Counterfactual Samples Synthesizing and Training) 的新的,不依赖模型的策略,以提高VQA模型的稳定性.
- 提高VQA模型的视觉解释性和问题敏感性,确保它们专注于相关的图像区域和语言细微差别.
主要方法:
- CSST包括两个阶段:反事实样本合成 (CSS) 和反事实样本培训 (CST).
- CSS通过掩盖关键的图像对象或问题词并赋予伪答案来生成修改过的数据.
- CST在原始和反事实样本上训练VQA模型,使用监督的对比损失和专门的样本选择机制来区分微妙的差异.
主要成果:
- CSST有效地迫使VQA模型关注所有关键的对象和单词.
- 用CSST训练的模型显示显著改善视觉解释性和问题敏感性.
- 在多个分布之外的基准标准上实现了最先进的性能,包括VQA-CP v2,VQA-CP v1和GQA-OOD.
结论:
- 拟议的CSST战略提供了一种强大而通用的方法,以减轻VQA模型中的语言偏差.
- CSST增强了基本的VQA能力,导致更可靠和可解释的AI系统.
- 该方法为VQA性能设定了新的基准,特别是在具有挑战性的分销外场景中.
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