Paper Info
Title
Robust Emotion Navigation: Few-shot Visual Sentiment Analysis by Auxiliary Noisy Data
Abstract
Few-shot visual sentiment analysis on social media is an important affective computing task. However, features acquired from few-shot samples are difficult, becasue the visual sentiment is a high-level integration task based on content and style. To address this issue, inspired by human learning processing, only a small number of multi-category emotions are learned from courses or specific occasions. In this paper, we propose a robust emotion navigation framework using auxiliary noisy data to re-focus on few-shot precise emotion knowledge. Firstly, we pre-trained the network on a large noisy data with cross-entropy loss, and the noise matrix can be estimated by predicted probability. Secondly, few-shot precise samples are applied as the prototype center to guide noisy data clustering. Here, the noise matrix is embedded into the loss function for re-weighting, which improves the noise robustness of the network. Finally, we relabel the noisy dataset with above joint training predictions and then re-train the network coarse-to-fine. We conduct experiments on three public sentiment datasets, including Sentibank, Twitter and Emotion6. The results demonstrate the effectiveness of the proposed method.
Year
DOI
Venue
2019
10.1109/ACIIW.2019.8925021
2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
Keywords
Field
DocType
Visual sentiment,few-shot,noisy data
Social media,Noise measurement,Task analysis,Communication,Visualization,Sentiment analysis,Computer science,Robustness (computer science),Artificial intelligence,Affective computing,Cluster analysis,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-7281-3892-3
0
0.34
References 
Authors
6
6
Name
Order
Citations
PageRank
Lin Wang101.01
Xiangmin Xu234322.97
Fang Liu300.68
Xiaofen Xing4246.79
Bolun Cai527016.48
Weirui Lu601.01