Cross-Domain Semi-Supervised Deep Metric Learning for Image Sentiment Analysis
Yun Liang, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama
Abstract
This paper presents a novel method on image sentiment analysis called cross-domain semi-supervised deep metric learning (CDSS-DML). The proposed method has two contributions. Firstly, since previous researches on image sentiment analysis suffer from the limit of a small amount of well-labeled data, which occurs a decrease in accuracy of classification, CDSS-DML breaks through the limit by training with unlabeled data based on a teacher-student model. Secondly, the proposed method overcomes the difficulty of distribution shift between well-labeled and unlabeled data by jointing three losses. Especially, the proposed method constructs an effective latent space with the joint loss considering the inter-class and the intra-class correlations for image sentiments. From experimental results, the performance improvement with CDSS-DML is confirmed.
BibTeX
@inproceedings{icassp2021_crossdomainsemis,
title = {Cross-Domain Semi-Supervised Deep Metric Learning for Image Sentiment Analysis},
author = {Yun Liang and Keisuke Maeda and Takahiro Ogawa and Miki Haseyama},
booktitle = {ICASSP 2021},
year = {2021}
}