视觉问题答案中的交叉模式偏见:与可能世界VQA的因果视图
Ali Vosoughi1, Shijian Deng2, Songyang Zhang3
1Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY 14620.
概括
本研究引入了一种新方法,通过同时解决视觉和语言混效应来减少视觉问题答案 (VQA) 系统的偏差,从而提高概括能力.
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:
- 视觉问题答案 (VQA) 系统通常依赖于语言和视觉之间的虚假相关性,阻碍了概括.
- 现有的去偏差方法一次处理一个模式,经常增加对方的偏差.
研究的目的:
- 在VQA中同时建模和减轻视觉和语言偏差的混效应.
- 通过减少双模态偏差来提高VQA模型的概括能力.
主要方法:
- 提出了一个反事实推断策略,以消除混效应的影响.
- 利用因果解释关系同时解决视觉和语言偏差.
- 开发了一个训练有素的模型,以有效地减少视觉和语言偏差.
主要成果:
- 提出的方法同时有效地减少视觉和语言偏差.
- 在数字答案的问题上实现了更高的准确性,这是VQA的一个开放问题.
- 在VQA-CP v2数据集上超越了最先进的方法.
结论:
- 这项工作是第一个使用因果解释关系在VQA中减少视觉和语言混效应的偏差.
- 开发的战略通过解决双模态偏差,增强了VQA模型的概括性.
- 该方法显示了显著的改进,特别是在VQA中的数值推理任务中.
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