Name
Title | ||
|---|---|---|
JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features. |
Abstract | ||
|---|---|---|
Learning social media content is the basis of many real-world applications, including information retrieval and recommendation systems, among others. In contrast with previous works that focus mainly on single modal or bi-modal learning, we propose to learn social media content by fusing jointly textual, acoustic, and visual information (JTAV). Effective strategies are proposed to extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We also introduce cross-modal fusion and attentive pooling techniques to integrate multi-modal information comprehensively. Extensive experimental evaluation conducted on real-world datasets demonstrates our proposed model outperforms the state-of-the-art approaches by a large margin. |
Year | Venue | DocType |
|---|---|---|
2018 | COLING | Conference |
Volume | Citations | PageRank |
abs/1806.01483 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongru Liang | 1 | 1 | 2.73 |
| Haozheng Wang | 2 | 9 | 2.19 |
| J. Wang | 3 | 93 | 36.24 |
| Shaodi You | 4 | 123 | 20.49 |
| Zhe Sun | 5 | 66 | 18.02 |
| Jin-Mao Wei | 6 | 135 | 15.90 |
| Zhenglu Yang | 7 | 257 | 35.45 |