半监督的VQA多模式解释通过自我批判的学习
概括
本研究引入了一种半监督的VQA多模式解释 (SME) 方法,以更清晰的VQA模型推理. 这种新的方法使用自我批评学习和半监督学习来提高解释的准确性和降低成本.
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) 解释旨在使VQA模型决策的人类可理解.
- 当前的方法通常依赖于单一的模式 (视觉或文本),导致模两可和逻辑不一致.
- 收集人类注释的解释是昂贵和耗时的.
研究的目的:
- 开发一种用于生成准确和逻辑一致的VQA解释的新方法.
- 解决单一模式范式的局限性,减少对昂贵的人类注释的依赖.
- 通过多模式解释,提高VQA模型的可解释性.
主要方法:
- 引入了一种半监督的VQA多模式解释 (SME) 方法.
- 采用自我批判性学习来提高答案和解释之间的逻辑一致性,使用奖励分数.
- 利用半监督学习来利用大型数据集,而无需人类注释的解释.
主要成果:
- 通过整合视觉和文本信息,中小企业方法有效地产生了全面的解释.
- 在VQA模型答案和它们生成的解释之间证明了更好的逻辑一致性.
- 在三个VQA解释数据集上实现了最先进的性能.
结论:
- 拟议的中小企业方法为VQA解释提供了更有效和高效的方法.
- 多模式解释和自我批判学习显著提高了解释性和一致性.
- 该方法利用未标记数据的能力降低了注释成本并提高了可扩展性.
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