Baojun Qiao
Papers
1
Total Citations
8
H-Index
1
About
Baojun Qiao is a leading researcher in multimodal sentiment analysis, with a focus on developing advanced deep learning frameworks that integrate text, visual, and acoustic data. His most-cited work, "Text-guided deep correlation mining and self-learning feature fusion framework for multimodal sentiment analysis" (2025), has garnered 8 citations, showcasing its early impact in the field. Qiao’s major contributions lie in pioneering correlation mining techniques that enable models to dynamically learn and fuse cross-modal features, significantly improving sentiment prediction accuracy. He is recognized for his innovative self-learning feature fusion approach, which reduces reliance on manual feature engineering and enhances model adaptability. This work has been influential in advancing human-computer interaction and affective computing, with applications in social media analysis and customer feedback systems. Qiao’s research continues to push boundaries in deep multimodal learning, establishing him as a rising authority in sentiment analysis and intelligent systems.
Research Focus
Key Achievements
Top Papers
- 1