| Paper ID | MMSP-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 |
| Session | MMSP-2: Deep Learning for Multimedia Analysis and Processing |
| Location | Gather.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 |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| 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. |
