Kaidi Meng
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Kaidi Meng has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Bin Liu
Dalian University of Technology, Dalian, Liaoning Province, China
1 shared publication - Boqian Liu
Dalian University of Technology, Dalian, China
1 shared publication - Haojie Li
Shandong University of Science and Technology
1 shared publication - Muyao Niu
Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, International School of Information Science and Engineering, Dalian University of Technology, Dalian, China
1 shared publication - Ning Wang
Dalian University of Technology
1 shared publication - Zhihui Wang
Dalian University of Technology
1 shared publication
Latest Publications
- 16 June 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE MultiMedia
- Vol. 28 (4) , 29-37
Semantic Place Prediction With User Attribute in Social Media
In our daily life, we prefer to describe the location of a person with semantic words such as “travel,” “cinema” rather than geographic terminology such as GPS coordinates ($32^{\circ }24^{^{\prime }}12^{^{\prime \prime }}$32∘24'12''N, $2^{\circ }90^{^{\prime }}26.5^{^{\prime \prime }}$2∘90'26.5''E). This is because semantic places are easier to understand compared with precise geographic location. Existing methods only take text-image pairs into consideration, neglecting the user attribute, which is essential in semantic place prediction. In this work, we explore the impact of user attribute and propose user attribute included multimodal fusion models for semantic place prediction. Specifically, due to the complementarity of different modalities, multimodal fusion networks can adaptively learn the relationship between different modalities to fully use representational information from each modality. Besides, we propose a multi-image processing module to solve the scenario that a piece of Microblog text is attached to multiple images. Extensive experiments on our collected dataset verified the effectiveness of the proposed approach.
- 16 June 2021
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE MultiMedia
- Vol. 28 (4) , 29-37
Semantic Place Prediction With User Attribute in Social Media
In our daily life, we prefer to describe the location of a person with semantic words such as “travel,” “cinema” rather than geographic terminology such as GPS coordinates ($32^{\circ }24^{^{\prime }}12^{^{\prime \prime }}$32∘24'12''N, $2^{\circ }90^{^{\prime }}26.5^{^{\prime \prime }}$2∘90'26.5''E). This is because semantic places are easier to understand compared with precise geographic location. Existing methods only take text-image pairs into consideration, neglecting the user attribute, which is essential in semantic place prediction. In this work, we explore the impact of user attribute and propose user attribute included multimodal fusion models for semantic place prediction. Specifically, due to the complementarity of different modalities, multimodal fusion networks can adaptively learn the relationship between different modalities to fully use representational information from each modality. Besides, we propose a multi-image processing module to solve the scenario that a piece of Microblog text is attached to multiple images. Extensive experiments on our collected dataset verified the effectiveness of the proposed approach.