协作模式融合,以缓解视觉问题答案中的语言偏差
Qiwen Lu1, Shengbo Chen1, Xiaoke Zhu1
1School of Computer and Information Engineering, Henan University, Kaifeng 475001, China.
Journal of imaging
|March 27, 2024
概括
本研究介绍了一种协作去偏差算法 (CoD),用于解决视觉问题答案 (VQA) 模型中的语言偏差. CoD 方法通过实现更好的多式联运知识融合,提高了模型的概括性和性能.
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,依赖于虚假的相关性而不是真正的多式联络理解.
- 这种偏差阻碍了模型的概括,并导致VQA任务的性能下降.
- 现有的方法很难有效地减轻这种固有的语言偏见.
研究的目的:
- 提出一种新的模式融合协作去偏差算法 (CoD),以解决VQA中的语言偏差.
- 通过使他们能够充分利用多式联运信息来提高VQA模型的概括能力.
- 通过减轻对表面相关性的依赖来提高VQA模型的预测准确性.
主要方法:
- 开发了一个协作去偏差算法 (CoD),将偏差视为模式信息忽视.
- 采用协作培训方法,以在不同模式之间进行相互建模.
- 实施了高效的功能融合,以整合视觉和文本模式的信息.
主要成果:
- 在多个VQA数据集 (VQA-CP v2,VQA v2,VQA-VS) 中证明了CoD算法的有效性.
- 与基线模型相比,实现了更好的性能和概括性.
- 使用CoD的基本基线模型在VQA-CP v2数据集上实现了60.14%的准确性.
结论:
- 拟议的CoD算法有效地解决了VQA模型中的语言偏差.
- 合作培训和模式融合对于利用多式联运知识和提高VQA绩效至关重要.
- CoD 方法为开发更强大,更普遍的 VQA 系统提供了一个有希望的方向.
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