Papers
1
Total Citations
5
H-Index
1
About
Yating Li is a researcher at the forefront of embodied artificial intelligence, specializing in human-robot interaction and affective computing. Her work addresses a critical challenge in EAI: enabling machines to accurately interpret human emotional states through facial expression recognition. Li’s most cited contribution, the development of MSAFNet (Multi-Scale Attention and Convolution-Transformer Fusion Network), introduces a novel deep learning architecture that dynamically integrates multi-scale attention mechanisms with transformer-based processing. This approach significantly enhances the robustness and real-time performance of facial expression recognition in dynamic, real-world environments—a key requirement for intuitive human-robot collaboration. Her research has already garnered early recognition, with her 2025 paper accumulating 5 citations shortly after publication, signaling growing impact in this emerging field. By bridging computer vision, deep learning, and robotics, Li is helping to lay the groundwork for more emotionally intelligent autonomous systems, where machines can perceive and respond to human affective cues with greater nuance and reliability.
Research Focus
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Top Papers
- 1