NLWSNet: a weakly supervised network for visual sentiment analysis in mislabeled web images
Affiliation(s): Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, China; Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace, Zhenjiang 212013, China; School of Computer Engineering, Nanjing Institute of Technology, Nanjing 211167, China; Center for Machine Vision and Signal Analysis, University of Oulu, Oulu 8000, Finland; less
收稿日期：
2019-11-12
录用日期：
2020-09-09
发布日期：
2020-09-09
摘要
Large-scale datasets are driving the rapid developments of deep convolutional neural networks for . 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 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 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.
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