计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 239-246.doi: 10.11896/jsjkx.201000171
张姝楠1,2, 曹峰1,2, 郭倩1,2, 钱宇华1,2,3
ZHANG Shu-nan1,2, CAO Feng1,2, GUO Qian1,2, QIAN Yu-hua1,2,3
摘要: 逻辑推理是人类智能的核心,是人工智能领域一个富有挑战性的研究课题。人类的IQ测试问题是衡量人类智商水平高低和逻辑推理能力的常用手段之一,如何让计算机学习拥有类似人类的逻辑推理能力是一个非常重要的研究内容,其目的是使计算机从给定的图像中直接学习逻辑推理模式,而无需事先为计算机设计先验推理模式。基于此目的,提出了一种新的数据集Fashion-IQ,该数据集中的每个样本包含7张输入图片和1个标签,这7张图片分别为3张包含一种或多种逻辑的问题输入图片和4张选项输入图片,目的是利用机器学习3张问题输入图片中包含的逻辑来预测下一张图片,从而选择正确的选项。为了解决这个问题,提出了一种时序关系模型。针对每个选项,该模型首先使用卷积神经网络提取前3张输入图片和选项图片的空间特征;接着采用关系网络将这4个空间特征两两组合;然后采用LSTM提取前3张问题输入图片和该选项的时序特征,将时序特征与组合好的空间特征相结合得到时序-空间融合特征;最后对前3张输入图片与每个选项得到的时序-空间融合特征进行进一步推理,采用softmax函数进行打分,得分最高的选项就是正确答案。实验结果证明,该模型在此数据集上实现了比较高的推理准确度。
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