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Visual sentiment analysis has received increasing attention in recent years.However, the dataset's quality is a concern because the sentiment labels arecrowd-sourcing, subjective, and prone to mistakes, and poses a severe threat tothe data-driven models, especially the deep neural networks. The deep modelswould generalize poorly on the testing cases when trained to over-fit thetraining samples with noisy sentiment labels. Inspired by the recent progresson learning with noisy labels, we propose a robust learning method to performrobust visual sentiment analysis. Our method relies on external memory toaggregate and filters noisy labels during training. The memory is composed ofthe prototypes with corresponding labels, which can be updated online. Thelearned prototypes and their labels can be regarded as denoising features andlabels for the local regions and can guide the training process to prevent themodel from overfitting the noisy cases. We establish a benchmark for visualsentiment analysis with label noise using publicly available datasets. Theexperiment results of the proposed benchmark settings comprehensively show theeffectiveness of our method.

Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis