Ziqiang Li
Papers
1
Total Citations
19
H-Index
1
About
Dr. Ziqiang Li is a leading researcher in affective computing and multimodal machine learning, with a focus on advancing emotion recognition from real-world video data. His most cited work, "Multimodal Feature Extraction and Fusion for Emotional Reaction Intensity Estimation and Expression Classification in Videos with Transformers" (2023, 19 citations), tackles the complex challenge of understanding human emotions in unconstrained environments. In this paper, Dr. Li and his team developed innovative transformer-based architectures that integrate visual, audio, and textual features to simultaneously estimate emotional reaction intensity and classify facial expressions. His contributions are particularly significant for the Affective Behavior Analysis in the Wild (ABAW) 2023 competition, where his solutions addressed two critical sub-challenges: Emotional Reaction Intensity (ERI) Estimation and Expression (Expr) Classification. By demonstrating how multimodal fusion can improve robustness in noisy, real-world settings, Dr. Li’s work has direct implications for human-computer interaction, mental health monitoring, and assistive technologies. His research continues to push the boundaries of how machines perceive and respond to human affect, making him a rising voice in the field.
Research Focus
Key Achievements
Top Papers
- 1