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反事实双偏差VQA:一个多模式偏差学习强大的视觉问题答案
概括
本研究引入了一种新的多式联络反事实双偏差模型,以解决视觉问题答案 (VQA) 模型中的语言偏差. 新方法有效地提高了VQA模型的稳定性,通过考虑视觉和问题反事实.
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) 模型经常表现出语言偏差,严重依赖问题,忽视图像内容.
- 现有的调解方法可能无法完全捕捉VQA中语言偏差的细微差别.
研究的目的:
- 提出一种新的多式联通反事实双偏差模型,以减轻VQA模型中的语言偏差.
- 提高VQA模型的可解释性和问题敏感性.
主要方法:
- 设计了一个共享参数化的双偏差模型,包含视觉和质疑反事实样本.
- 在使用交叉和KL分歧损失的双偏差模型训练期间结目标VQA模型参数.
- 重新训练了目标VQA模型,使用从冷双偏差模型和保证金损失中生成的伪标签.
主要成果:
- 拟议的反事实双偏模式在VQA-CP数据集上表现出卓越的有效性.
- 对VQA v2数据集的不满意性能进行了分析.
结论:
- 多模式反事实双偏模式有效地解决了VQA中的语言偏差.
- 该方法通过强调相关的视觉和文本信息来提高VQA模型的稳定性和准确性.
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