Large-scale datasets are driving the rapid developments of deep convolutional neural networks for visual sentiment analysis. However, the annotation of large-scale datasets is expensive and time consuming. Instead, it is easy to obtain weakly labeled web images from the Internet. However, noisy labels still lead to seriously degraded performance when we use images directly from the web for training networks. To address this drawback, we propose an end-to-end weakly supervised learning network, which is robust to mislabeled web images. Specifically, the proposed attention module automatically eliminates the distraction of those samples with incorrect labels by reducing their attention scores in the training process. On the other hand, the special-class activation map module is designed to stimulate the network by focusing on the significant regions from the samples with correct labels in a weakly supervised learning approach. Besides the process of feature learning, applying regularization to the classifier is considered to minimize the distance of those samples within the same class and maximize the distance between different class centroids. Quantitative and qualitative evaluations on well- and mislabeled web image datasets demonstrate that the proposed algorithm outperforms the related methods.

大规模数据集正在推动用于视觉情感分析的深度卷积神经网络的快速发展。但是，大规模数据集的注释既昂贵又耗时。取而代之的是，很容易从Internet获得标记较弱的Web图像。但是，当我们直接将网络中的图像用于培训网络时，嘈杂的标签仍然会导致性能严重下降。为了解决这个缺点，我们提出了一种端到端的弱监督学习网络，该网络对于标签错误的Web图像很健壮。具体来说，建议的注意力模块通过减少训练过程中的注意力得分，自动消除那些带有错误标签的样本的干扰。另一方面，特殊类别的激活图模块旨在通过在弱监督学习方法中关注带有正确标签的样本的重要区域来刺激网络。除了特征学习的过程外，还考虑将正则化应用到分类器上，以最大程度地减少相同类别内这些样本的距离，并最大化不同类别质心之间的距离。对标签正确和标签错误的网页图像数据集进行定量和定性评估表明，该算法优于相关方法。将正则化应用于分类器被认为是最小化相同类别内的那些样本的距离并且最大化不同类别质心之间的距离。对标签正确和标签错误的网页图像数据集进行定量和定性评估表明，该算法优于相关方法。将正则化应用于分类器被认为是最小化相同类别内的那些样本的距离并且最大化不同类别质心之间的距离。对标签正确和标签错误的网页图像数据集进行定量和定性评估表明，该算法优于相关方法。