2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDMMSP-2.1
Paper Title Cross-Domain Semi-Supervised Deep Metric Learning for Image Sentiment Analysis
Authors Yun Liang, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama, Hokkaido University, Japan
SessionMMSP-2: Deep Learning for Multimedia Analysis and Processing
LocationGather.Town
Session Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Time:Tuesday, 08 June, 14:00 - 14:45
Presentation Poster
Topic Multimedia Signal Processing: Emerging Areas in Multimedia
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.