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整合神经符号推理与变化的因果推理网络用于解释性视觉问题答案
概括
本研究介绍了用于解释性视觉问题答案 (EVQA) 的程序引导变异因果推理网络 (Pro-VCIN). 通过整合神经符号方法和因果推理,Pro-VCIN提高了推理的可解释性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 解释性视觉问题答案 (EVQA) 通过要求解释推理来增强传统的VQA.
- 目前的EVQA模型使用黑盒神经网络,限制推理过程的解释性.
- 现有的模型往往独立地预测答案和解释,忽视它们的因果关系.
研究的目的:
- 为EVQA开发一种新的模型,解决当前黑子方法的局限性.
- 提高多式联络推理的可解释性和可验证性.
- 在EVQA.QA中建立答案和解释之间的因果关系.
主要方法:
- 提出了程序引导的变异性因果推理网络 (Pro-VCIN).
- 综合神经符号推理与变异性因果推理.
- 使用预训练模型来提取特征和将问题转换为程序.
- 采用多模式程序变压器来生成解释.
- 实施了变异性因果推理来建模因果关系并预测答案.
主要成果:
- 在EVQA基准数据集上,Pro-VCIN表现优越.
- 与最先进的方法相比,该模型实现了增强的解释性.
- 在预测的答案和解释之间建立了因果关系.
结论:
- 亲VCIN有效地整合神经符号推理和因果推理,以改善EVQA.
- 拟议的方法提高了多式联络推理系统的性能和可解释性.
- 这项工作为视觉问题答案提供了更透明和可验证的方法.
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